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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
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<title>Chunhua Shen</title>
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<li><a href="index.html" target="_self">Home</a></li>
<li><a href="paper.html" target="_self">Publications</a></li>
<li><a href="teaching.html" target="_self">Teaching</a></li>
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<div id="toptitle">
<h1>Selected Publications</h1>
<div id="subtitle">Categorised <a href="paper.html" target=“blank”>by year <i class='fa fa-clock-o' aria-hidden='true'></i></a>, <a href="paper2.html" target=“blank”>by venue <i class='fa fa-location-arrow' aria-hidden='true'></i></a>.
<a href="fullpaper.html" target=“blank”>Full publication list <i class='fa fa-list-ul' aria-hidden='true'></i></a>. <a href="cshen_papers.pdf" target=“blank”>Full list (PDF) <i class='fa fa-file-pdf-o' aria-hidden='true'></i></a>.
</div>
</div>
<p><a href="https://scholar.google.com/citations?hl=en&user=Ljk2BvIAAAAJ&view_op=list_works&pagesize=100" target=“blank”>Google scholar (76530 citations) <i class='ai ai-google-scholar' aria-hidden='true'></i></a>,
<a href="https://dblp.org/pid/56/1673.html" target=“blank”>DBLP <i class='ai ai-dblp ai-1x'></i></a>,
<a href="https://arxiv.org/a/shen_c_1.html" target=“blank”>arXiv <i class='ai ai-arxiv ai-1x'></i></a>.
</p>
<h2>Journal: 69</h2>
<h3><a href="https://dblp.org/search/publ?q=author%3AChunhua_Shen%3Avenue%3A%20Trans%20Pattern%20Anal%20Mach%20Intell%20" target=“blank”>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</a>: 43</h3>
<h3><a href="https://dblp.org/search/publ?q=author%3AChunhua_Shen%3Avenue%3A%20Int%20J%20Comput%20Vis%20" target=“blank”>International Journal of Computer Vision (IJCV)</a>: 26</h3>
<p><br />
</p>
<ol reversed>
<li><p><b>Masked channel modeling for bootstrapping visual pre-training</b>
<br />\(\cdot\) <i>Y. Liu, X. Wang, M. Zhu, Y. Cao, T. Huang, C. Shen</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Liuyang2024Masked.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Masked+Channel+Modeling+for+Bootstrapping+Visual+Pre-training+Liu,+Yang+and+Wang,+Xinlong+and+Zhu,+Muzhi+and+Cao,+Yue+and+Huang,+Tiejun+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Masked+Channel+Modeling+for+Bootstrapping+Visual+Pre-training" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>End-to-end video text spotting with Transformer</b>
<br />\(\cdot\) <i>W. Wu, C. Shen, Y. Cai, D. Zhang, Y. Fu, P. Luo, H. Zhou</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Wu2022transdetr.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=End-to-End+Video+Text+Spotting+with+{T}ransformer+Wu,+Weijia+and+Shen,+Chunhua+and+Cai,+Yuanqiang+and+Zhang,+Debing+and+Fu,+Ying+and+Luo,+Ping+and+Zhou,+Hong" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=End-to-End+Video+Text+Spotting+with+{T}ransformer" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Towards robust monocular depth estimation: a new baseline and benchmark</b>
<br />\(\cdot\) <i>K. Xian, Z. Cao, C. Shen, G. Lin</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Depth2024Xian.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Towards+robust+monocular+depth+estimation:+a+new+baseline+and+benchmark+Xian,+Ke+and+Cao,+Zhiguo+and+Shen,+Chunhua+and+Lin,+Guosheng" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Towards+robust+monocular+depth+estimation:+a+new+baseline+and+benchmark" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Scaling up multi-domain semantic segmentation with sentence embeddings</b>
<br />\(\cdot\) <i>W. Yin, Y. Liu, C. Shen, B. Sun, A. van den Hengel</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Yin2024SIW.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Scaling+Up+Multi-domain+Semantic+Segmentation+with+Sentence+Embeddings+Yin,+Wei+and+Liu,+Yifan+and+Shen,+Chunhua+and+Sun,+Baichuan+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Scaling+Up+Multi-domain+Semantic+Segmentation+with+Sentence+Embeddings" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Metric3D v2: a versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation</b>
<br />\(\cdot\) <i>M. Hu, W. Yin, C. Zhang, Z. Cai, X. Long, H. Chen, K. Wang, G. Yu, C. Shen, S. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2404.15506" target=“blank”>arXiv</a><a href="data/bibtex/hu2024metric3dv2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={Metric3D}+v2:+A+Versatile+Monocular+Geometric+Foundation+Model+for+Zero-shot+Metric+Depth+and+Surface+Normal+Estimation+Hu,+Mu+and+Yin,+Wei+and+Zhang,+Chi+and+Cai,+Zhipeng+and+Long,+Xiaoxiao+and+Chen,+Hao+and+Wang,+Kaixuan+and+Yu,+Gang+and+Shen,+Chunhua+and+Shen,+Shaojie" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={Metric3D}+v2:+A+Versatile+Monocular+Geometric+Foundation+Model+for+Zero-shot+Metric+Depth+and+Surface+Normal+Estimation" target=“blank”>semantic scholar</a><a href="https://jugghm.github.io/Metric3Dv2/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Self-supervised 3d scene flow estimation and motion prediction using local rigidity prior</b>
<br />\(\cdot\) <i>R. Li, C. Zhang, Z. Wang, C. Shen, G. Lin</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2310.11284" target=“blank”>arXiv</a><a href="data/bibtex/Ruibo2024TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Self-Supervised+3D+Scene+Flow+Estimation+and+Motion+Prediction+using+Local+Rigidity+Prior+Li,+Ruibo+and+Zhang,+Chi+and+Wang,+Zhe+and+Shen,+Chunhua+and+Lin,+Guosheng" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Self-Supervised+3D+Scene+Flow+Estimation+and+Motion+Prediction+using+Local+Rigidity+Prior" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Lin2023IJCVSuperxxxarXiv.jpg"><b>Super vision transformer</b>
<br />\(\cdot\) <i>M. Lin, M. Chen, Y. Zhang, C. Shen, R. Ji, L. Cao</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2205.11397" target=“blank”>arXiv</a><a href="data/bibtex/Lin2023IJCVSuper.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Super+Vision+Transformer+Lin,+Mingbao+and+Chen,+Mengzhao+and+Zhang,+Yuxin+and+Shen,+Chunhua+and+Ji,+Rongrong+and+Cao,+Liujuan" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Super+Vision+Transformer" target=“blank”>semantic scholar</a><a href="https://github.com/lmbxmu/SuperViT" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>A dynamic feature interaction framework for multi-task visual perception</b>
<br />\(\cdot\) <i>Y. Xi, H. Chen, N. Wang, P. Wang, Y. Zhang, C. Shen, Y. Liu</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/XiY2023IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=A+Dynamic+Feature+Interaction+Framework+for+Multi-task+Visual+Perception+Xi,+Yuling+and+Chen,+Hao+and+Wang,+Ning+and+Wang,+Peng+and+Zhang,+Yanning+and+Shen,+Chunhua+and+Liu,+Yifan" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=A+Dynamic+Feature+Interaction+Framework+for+Multi-task+Visual+Perception" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Lu2023IJCVCountingxxxarXiv.jpg"><b>From open set to closed set: supervised spatial divide-and-conquer for object counting</b>
<br />\(\cdot\) <i>H. Xiong, H. Lu, C. Liu, L. Liu, C. Shen, Z. Cao</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2001.01886" target=“blank”>arXiv</a><a href="data/bibtex/Lu2023IJCVCounting.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=From+Open+Set+to+Closed+Set:+Supervised+Spatial+Divide-and-Conquer+for+Object+Counting+Xiong,+Haipeng+and+Lu,+Hao+and+Liu,+Chengxin+and+Liu,+Liang+and+Shen,+Chunhua+and+Cao,+Zhiguo" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=From+Open+Set+to+Closed+Set:+Supervised+Spatial+Divide-and-Conquer+for+Object+Counting" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>SPL-Net: spatial-semantic patch learning network for facial attribute recognition with limited labeled data</b>
<br />\(\cdot\) <i>Y. Yan, Y. Shu, S. Chen, J. Xue, C. Shen, H. Wang</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/YAN2023IJCVSPL.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SPL-Net}:+Spatial-Semantic+Patch+Learning+Network+for+Facial+Attribute+Recognition+with+Limited+Labeled+Data+Yan,+Yan+and+Shu,+Ying+and+Chen,+Si+and+Xue,+Jing-Hao+and+Shen,+Chunhua+and+Wang,+Hanzi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SPL-Net}:+Spatial-Semantic+Patch+Learning+Network+for+Facial+Attribute+Recognition+with+Limited+Labeled+Data" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Zhang2023SegVITv2xxxarXiv.jpg"><b>SegViT v2: exploring efficient and continual semantic segmentation with plain vision transformers</b>
<br />\(\cdot\) <i>B. Zhang, L. Liu, M. Phan, Z. Tian, C. Shen, Y. Liu</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2306.06289" target=“blank”>arXiv</a><a href="data/bibtex/Zhang2023SegVITv2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SegViT}+v2:+Exploring+Efficient+and+Continual+Semantic+Segmentation+with+Plain+Vision+Transformers+Zhang,+Bowen+and+Liu,+Liyang+and+Phan,+Minh+Hieu+and+Tian,+Zhi+and+Shen,+Chunhua+and+Liu,+Yifan" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SegViT}+v2:+Exploring+Efficient+and+Continual+Semantic+Segmentation+with+Plain+Vision+Transformers" target=“blank”>semantic scholar</a><a href="https://github.com/zbwxp/SegVit" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Liu2023TPAMIxxxarXiv.jpg"><b>Single-path bit sharing for automatic loss-aware model compression</b>
<br />\(\cdot\) <i>J. Liu, B. Zhuang, P. Chen, C. Shen, J. Cai, M. Tan</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2101.04935" target=“blank”>arXiv</a><a href="data/bibtex/Liu2023TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Single-path+Bit+Sharing+for+Automatic+Loss-aware+Model+Compression+Liu,+Jing+and+Zhuang,+Bohan+and+Chen,+Peng+and+Shen,+Chunhua+and+Cai,+Jianfei+and+Tan,+Mingkui" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Single-path+Bit+Sharing+for+Automatic+Loss-aware+Model+Compression" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/SPTSv2xxxarXiv.jpg"><b>SPTS v2: single-point scene text spotting</b>
<br />\(\cdot\) <i>Y. Liu, J. Zhang, D. Peng, M. Huang, X. Wang, J. Tang, C. Huang, D. Lin, C. Shen, X. Bai, L. Jin</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2301.01635" target=“blank”>arXiv</a><a href="data/bibtex/SPTSv2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SPTS+v2}:+Single-Point+Scene+Text+Spotting+Liu,+Yuliang+and+Zhang,+Jiaxin+and+Peng,+Dezhi+and+Huang,+Mingxin+and+Wang,+Xinyu+and+Tang,+Jingqun+and+Huang,+Can+and+Lin,+Dahua+and+Shen,+Chunhua+and+Bai,+Xiang+and+Jin,+Lianwen" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SPTS+v2}:+Single-Point+Scene+Text+Spotting" target=“blank”>semantic scholar</a><a href="https://github.com/Yuliang-Liu/SPTSv2" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Sun2023TPAMIxxxarXiv.jpg"><b>SC-DepthV3: robust self-supervised monocular depth estimation for dynamic scenes</b>
<br />\(\cdot\) <i>L. Sun, J. Bian, H. Zhan, W. Yin, I. Reid, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2211.03660" target=“blank”>arXiv</a><a href="data/bibtex/Sun2023TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SC-DepthV3}:+Robust+Self-supervised+Monocular+Depth+Estimation+for+Dynamic+Scenes+Sun,+Libo+and+Bian,+Jia-Wang+and+Zhan,+Huangying+and+Yin,+Wei+and+Reid,+Ian+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SC-DepthV3}:+Robust+Self-supervised+Monocular+Depth+Estimation+for+Dynamic+Scenes" target=“blank”>semantic scholar</a><a href="https://github.com/JiawangBian/sc_depth_pl" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Xie2023DodNetxxxarXiv.jpg"><b>Learning from partially labeled data for multi-organ and tumor segmentation</b>
<br />\(\cdot\) <i>Y. Xie, J. Zhang, Y. Xia, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2211.06894" target=“blank”>arXiv</a><a href="data/bibtex/Xie2023DodNet.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+from+partially+labeled+data+for+multi-organ+and+tumor+segmentation+Xie,+Yutong+and+Zhang,+Jianpeng+and+Xia,+Yong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+from+partially+labeled+data+for+multi-organ+and+tumor+segmentation" target=“blank”>semantic scholar</a><a href="https://git.io/DoDNet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Zhuang2022IJCVxxxarXiv.jpg"><b>Structured binary neural networks for image recognition</b>
<br />\(\cdot\) <i>B. Zhuang, C. Shen, M. Tan, P. Chen, L. Liu, I. Reid</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1909.09934" target=“blank”>arXiv</a><a href="data/bibtex/Zhuang2022IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Structured+Binary+Neural+Networks+for+Image+Recognition+Zhuang,+Bohan+and+Shen,+Chunhua+and+Tan,+Mingkui+and+Chen,+Peng+and+Liu,+Lingqiao+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Structured+Binary+Neural+Networks+for+Image+Recognition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Dynamic convolution for 3D point cloud instance segmentation</b>
<br />\(\cdot\) <i>T. He, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2107.08392" target=“blank”>arXiv</a><a href="data/bibtex/Tong2022TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Dynamic+Convolution+for+{3D}+Point+Cloud+Instance+Segmentation+He,+Tong+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Dynamic+Convolution+for+{3D}+Point+Cloud+Instance+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>FCOS: a simple and strong anchor-free object detector</b>
<br />\(\cdot\) <i>Z. Tian, C. Shen, H. Chen, T. He</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022</i>.
<br />\(\cdot\) <a href="https://doi.org/10.1109/TPAMI.2020.3032166" target=“blank”>link</a><a href="data/bibtex/TianSCH22.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FCOS:}+A+Simple+and+Strong+Anchor-Free+Object+Detector+Tian,+Zhi+and+Shen,+Chunhua+and+Chen,+Hao+and+He,+Tong" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FCOS:}+A+Simple+and+Strong+Anchor-Free+Object+Detector" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/AdelaiDet/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/CondInst2022TianxxxarXiv.jpg"><b>Instance and panoptic segmentation using conditional convolutions</b>
<br />\(\cdot\) <i>Z. Tian, B. Zhang, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2102.03026" target=“blank”>arXiv</a><a href="data/bibtex/CondInst2022Tian.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Instance+and+Panoptic+Segmentation+Using+Conditional+Convolutions+Tian,+Zhi+and+Zhang,+Bowen+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Instance+and+Panoptic+Segmentation+Using+Conditional+Convolutions" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/AdelaiDet/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Weiyin2022TPAMIxxxarXiv.jpg"><b>Towards accurate reconstruction of 3D scene shape from a single monocular image</b>
<br />\(\cdot\) <i>W. Yin, J. Zhang, O. Wang, S. Niklaus, S. Chen, Y. Liu, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2208.13241" target=“blank”>arXiv</a><a href="data/bibtex/Weiyin2022TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Towards+Accurate+Reconstruction+of+{3D}+Scene+Shape+from+A+Single+Monocular+Image+Yin,+Wei+and+Zhang,+Jianming+and+Wang,+Oliver+and+Niklaus,+Simon+and+Chen,+Simon+and+Liu,+Yifan+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Towards+Accurate+Reconstruction+of+{3D}+Scene+Shape+from+A+Single+Monocular+Image" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/depth/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Chi2022TPAMIxxxarXiv.jpg"><b>DeepEMD: differentiable earth mover's distance for few-shot learning</b>
<br />\(\cdot\) <i>C. Zhang, Y. Cai, G. Lin, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2003.06777" target=“blank”>arXiv</a><a href="data/bibtex/Chi2022TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DeepEMD}:+Differentiable+Earth+Mover's+Distance+for+Few-Shot+Learning+Zhang,+Chi+and+Cai,+Yujun+and+Lin,+Guosheng+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DeepEMD}:+Differentiable+Earth+Mover's+Distance+for+Few-Shot+Learning" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2105.11610.pdf"><img class="imgP right" src="data/thumbnail/Bian2021IJCVxxxarXiv.jpg"></a><b>Unsupervised scale-consistent depth learning from video</b>
<br />\(\cdot\) <i>J. Bian, H. Zhan, N. Wang, Z. Li, L. Zhang, C. Shen, M. Cheng, I. Reid</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2105.11610" target=“blank”>arXiv</a><a href="data/bibtex/Bian2021IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Unsupervised+Scale-consistent+Depth+Learning+from+Video+Bian,+Jia-Wang+and+Zhan,+Huangying+and+Wang,+Naiyan+and+Li,+Zhichao+and+Zhang,+Le+and+Shen,+Chunhua+and+Cheng,+Ming-Ming+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Unsupervised+Scale-consistent+Depth+Learning+from+Video" target=“blank”>semantic scholar</a><a href="https://github.com/JiawangBian/SC-SfMLearner-Release" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Joint classification and regression for visual tracking with fully convolutional Siamese networks</b>
<br />\(\cdot\) <i>Y. Cui, D. Guo, Y. Shao, Z. Wang, C. Shen, L. Zhang, S. Chen</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Cui2021Joint.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Joint+classification+and+regression+for+visual+tracking+with+fully+convolutional+{S}iamese+networks+Cui,+Ying+and+Guo,+Dongyan+and+Shao,+Yanyan+and+Wang,+Zhenhua+and+Shen,+Chunhua+and+Zhang,+Liyan+and+Chen,+Shengyong" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Joint+classification+and+regression+for+visual+tracking+with+fully+convolutional+{S}iamese+networks" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/IJCV2021LiuylxxxarXiv.jpg"><b>Exploring the capacity of an orderless box discretization network for multi-orientation scene text detection</b>
<br />\(\cdot\) <i>Y. Liu, T. He, H. Chen, X. Wang, C. Luo, S. Zhang, C. Shen, L. Jin</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1912.09629" target=“blank”>arXiv</a><a href="data/bibtex/IJCV2021Liuyl.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Exploring+the+Capacity+of+an+Orderless+Box+Discretization+Network+for+Multi-orientation+Scene+Text+Detection+Liu,+Yuliang+and+He,+Tong+and+Chen,+Hao+and+Wang,+Xinyu+and+Luo,+Canjie+and+Zhang,+Shuaitao+and+Shen,+Chunhua+and+Jin,+Lianwen" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Exploring+the+Capacity+of+an+Orderless+Box+Discretization+Network+for+Multi-orientation+Scene+Text+Detection" target=“blank”>semantic scholar</a><a href="https://git.io/TextDet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>NAS-FCOS: efficient search for object detection architectures</b>
<br />\(\cdot\) <i>N. Wang, Y. Gao, H. Chen, P. Wang, Z. Tian, C. Shen, Y. Zhang</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Wang2021IJCV_NAS.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={NAS-FCOS}:+Efficient+Search+for+Object+Detection+Architectures+Wang,+Ning+and+Gao,+Yang+and+Chen,+Hao+and+Wang,+Peng+and+Tian,+Zhi+and+Shen,+Chunhua+and+Zhang,+Yanning" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={NAS-FCOS}:+Efficient+Search+for+Object+Detection+Architectures" target=“blank”>semantic scholar</a><a href="https://github.com/Lausannen/NAS-FCOS" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>A dual-attention-guided network for ghost-free high dynamic range imaging</b>
<br />\(\cdot\) <i>Q. Yan, D. Gong, Q. Shi, A. van den Hengel, C. Shen, I. Reid, Y. Zhang</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Yan2021Ghostfree.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=A+Dual-Attention-guided+network+for+ghost-free+high+dynamic+range+imaging+Yan,+Qingsen+and+Gong,+Dong+and+Shi,+Qinfeng+and+{van+den+Hengel},+Anton+and+Shen,+Chunhua+and+Reid,+Ian+and+Zhang,+Yanning" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=A+Dual-Attention-guided+network+for+ghost-free+high+dynamic+range+imaging" target=“blank”>semantic scholar</a><a href="https://github.com/qingsenyangit/AHDRNet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Yu2021BiSegV2xxxarXiv.jpg"><b>BiSeNet v2: bilateral network with guided aggregation for real-time semantic segmentation</b>
<br />\(\cdot\) <i>C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, N. Sang</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2004.02147" target=“blank”>arXiv</a><a href="data/bibtex/Yu2021BiSegV2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={BiSeNet}+v2:+Bilateral+Network+with+Guided+Aggregation+for+Real-time+Semantic+Segmentation+Yu,+Changqian+and+Gao,+Changxin+and+Wang,+Jingbo+and+Yu,+Gang+and+Shen,+Chunhua+and+Sang,+Nong" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={BiSeNet}+v2:+Bilateral+Network+with+Guided+Aggregation+for+Real-time+Semantic+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Haokui2021NASxxxarXiv.jpg"><b>Memory-efficient hierarchical neural architecture search for image restoration</b>
<br />\(\cdot\) <i>H. Zhang, Y. Li, H. Chen, C. Gong, Z. Bai, C. Shen</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2012.13212" target=“blank”>arXiv</a><a href="data/bibtex/Haokui2021NAS.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Memory-Efficient+Hierarchical+Neural+Architecture+Search+for+Image+Restoration+Zhang,+Haokui+and+Li,+Ying+and+Chen,+Hao+and+Gong,+Chengrong+and+Bai,+Zongwen+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Memory-Efficient+Hierarchical+Neural+Architecture+Search+for+Image+Restoration" target=“blank”>semantic scholar</a><a href="https://github.com/hkzhang91/HiNAS" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Auto-rectify network for unsupervised indoor depth estimation</b>
<br />\(\cdot\) <i>J. Bian, H. Zhan, N. Wang, T. Chin, C. Shen, I. Reid</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Autorectify2021Bian.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Auto-Rectify+Network+for+Unsupervised+Indoor+Depth+Estimation+Bian,+Jia-Wang+and+Zhan,+Huangying+and+Wang,+Naiyan+and+Chin,+Tat-Jun+and+Shen,+Chunhua+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Auto-Rectify+Network+for+Unsupervised+Indoor+Depth+Estimation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Liu2021ABCNetv2xxxarXiv.jpg"><b>ABCNet v2: adaptive bezier-curve network for real-time end-to-end text spotting</b>
<br />\(\cdot\) <i>Y. Liu, C. Shen, L. Jin, T. He, P. Chen, C. Liu, H. Chen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2105.03620" target=“blank”>arXiv</a><a href="data/bibtex/Liu2021ABCNetv2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={ABCNet}+v2:+Adaptive+Bezier-Curve+Network+for+Real-time+End-to-end+Text+Spotting+Liu,+Yuliang+and+Shen,+Chunhua+and+Jin,+Lianwen+and+He,+Tong+and+Chen,+Peng+and+Liu,+Chongyu+and+Chen,+Hao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={ABCNet}+v2:+Adaptive+Bezier-Curve+Network+for+Real-time+End-to-end+Text+Spotting" target=“blank”>semantic scholar</a><a href="https://git.io/AdelaiDet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Li2021TextxxxarXiv.jpg"><b>Towards end-to-end text spotting in natural scenes</b>
<br />\(\cdot\) <i>P. Wang, H. Li, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1906.06013" target=“blank”>arXiv</a><a href="data/bibtex/Li2021Text.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Towards+End-to-End+Text+Spotting+in+Natural+Scenes+Wang,+Peng+and+Li,+Hui+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Towards+End-to-End+Text+Spotting+in+Natural+Scenes" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Wang2021PANplusxxxarXiv.jpg"><b>PAN++: towards efficient and accurate end-to-end spotting of arbitrarily-shaped text</b>
<br />\(\cdot\) <i>W. Wang, E. Xie, X. Li, X. Liu, D. Liang, Z. Yang, T. Lu, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2105.00405" target=“blank”>arXiv</a><a href="data/bibtex/Wang2021PANplus.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={PAN++}:+Towards+Efficient+and+Accurate+End-to-End+Spotting+of+Arbitrarily-Shaped+Text+Wang,+Wenhai+and+Xie,+Enze+and+Li,+Xiang+and+Liu,+Xuebo+and+Liang,+Ding+and+Yang,+Zhibo+and+Lu,+Tong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={PAN++}:+Towards+Efficient+and+Accurate+End-to-End+Spotting+of+Arbitrarily-Shaped+Text" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/WXL2021SOLOxxxarXiv.jpg"><b>SOLO: a simple framework for instance segmentation</b>
<br />\(\cdot\) <i>X. Wang, R. Zhang, C. Shen, T. Kong, L. Li</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2106.15947" target=“blank”>arXiv</a><a href="data/bibtex/WXL2021SOLO.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SOLO}:+A+Simple+Framework+for+Instance+Segmentation+Wang,+Xinlong+and+Zhang,+Rufeng+and+Shen,+Chunhua+and+Kong,+Tao+and+Li,+Lei" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SOLO}:+A+Simple+Framework+for+Instance+Segmentation" target=“blank”>semantic scholar</a><a href="https://git.io/AdelaiDet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Yin2021PAMIvnxxxarXiv.jpg"><b>Virtual normal: enforcing geometric constraints for accurate and robust depth prediction</b>
<br />\(\cdot\) <i>W. Yin, Y. Liu, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2103.04216" target=“blank”>arXiv</a><a href="data/bibtex/Yin2021PAMIvn.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Virtual+Normal:+Enforcing+Geometric+Constraints+for+Accurate+and+Robust+Depth+Prediction+Yin,+Wei+and+Liu,+Yifan+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Virtual+Normal:+Enforcing+Geometric+Constraints+for+Accurate+and+Robust+Depth+Prediction" target=“blank”>semantic scholar</a><a href="https://git.io/Depth" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Zhuang2021QuantizationxxxarXiv.jpg"><b>Effective training of convolutional neural networks with low-bitwidth weights and activations</b>
<br />\(\cdot\) <i>B. Zhuang, J. Liu, M. Tan, L. Liu, I. Reid, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1908.04680" target=“blank”>arXiv</a><a href="data/bibtex/Zhuang2021Quantization.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Effective+Training+of+Convolutional+Neural+Networks+with+Low-bitwidth+Weights+and+Activations+Zhuang,+Bohan+and+Liu,+Jing+and+Tan,+Mingkui+and+Liu,+Lingqiao+and+Reid,+Ian+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Effective+Training+of+Convolutional+Neural+Networks+with+Low-bitwidth+Weights+and+Activations" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Luo2020IJCVxxxarXiv.jpg"><b>Separating content from style using adversarial learning for recognizing text in the wild</b>
<br />\(\cdot\) <i>C. Luo, Q. Lin, Y. Liu, L. Jin, C. Shen</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2001.04189" target=“blank”>arXiv</a><a href="data/bibtex/Luo2020IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Separating+Content+from+Style+Using+Adversarial+Learning+for+Recognizing+Text+in+the+Wild+Luo,+Canjie+and+Lin,+Qingxiang+and+Liu,+Yuliang+and+Jin,+Lianwen+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Separating+Content+from+Style+Using+Adversarial+Learning+for+Recognizing+Text+in+the+Wild" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2008.00942.pdf"><img class="imgP right" src="data/thumbnail/Cao2020GANxxxarXiv.jpg"></a><b>Improving generative adversarial networks with local coordinate coding</b>
<br />\(\cdot\) <i>J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, M. Tan</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2008.00942" target=“blank”>arXiv</a><a href="data/bibtex/Cao2020GAN.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Improving+Generative+Adversarial+Networks+with+Local+Coordinate+Coding+Cao,+Jiezhang+and+Guo,+Yong+and+Wu,+Qingyao+and+Shen,+Chunhua+and+Huang,+Junzhou+and+Tan,+Mingkui" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Improving+Generative+Adversarial+Networks+with+Local+Coordinate+Coding" target=“blank”>semantic scholar</a><a href="https://github.com/SCUTjinchengli/LCCGAN-v2" target=“blank”>project webpage</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1711.00253.pdf"><img class="imgP right" src="data/thumbnail/Chen2019PAMIxxxarXiv.jpg"></a><b>Adversarial learning of structure-aware fully convolutional networks for landmark localization</b>
<br />\(\cdot\) <i>Y. Chen, C. Shen, H. Chen, X. Wei, L. Liu, J. Yang</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1711.00253" target=“blank”>arXiv</a><a href="https://doi.org/10.1109/TPAMI.2019.2901875" target=“blank”>link</a><a href="data/bibtex/Chen2019PAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Adversarial+Learning+of+Structure-Aware+Fully+Convolutional+Networks+for+Landmark+Localization+Chen,+Yu+and+Shen,+Chunhua+and+Chen,+Hao+and+Wei,+Xiu-Shen+and+Liu,+Lingqiao+and+Yang,+Jian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Adversarial+Learning+of+Structure-Aware+Fully+Convolutional+Networks+for+Landmark+Localization" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Liu2020PAMIxxxarXiv.jpg"><b>Structured knowledge distillation for dense prediction</b>
<br />\(\cdot\) <i>Y. Liu, C. Shun, J. Wang, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1903.04197" target=“blank”>arXiv</a><a href="https://ieeexplore.ieee.org/document/9115859" target=“blank”>link</a><a href="data/bibtex/Liu2020PAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Structured+Knowledge+Distillation+for+Dense+Prediction+Liu,+Yifan+and+Shun,+Changyong+and+Wang,+Jingdong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Structured+Knowledge+Distillation+for+Dense+Prediction" target=“blank”>semantic scholar</a><a href="https://github.com/irfanICMLL/structure_knowledge_distillation" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Lu2020PAMIIndexNetxxxarXiv.jpg"><b>Index networks</b>
<br />\(\cdot\) <i>H. Lu, Y. Dai, C. Shen, S. Xu</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1908.09895" target=“blank”>arXiv</a><a href="https://doi.org/10.1109/TPAMI.2020.3004474" target=“blank”>link</a><a href="data/bibtex/Lu2020PAMIIndexNet.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Index+Networks+Lu,+Hao+and+Dai,+Yutong+and+Shen,+Chunhua+and+Xu,+Songcen" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Index+Networks" target=“blank”>semantic scholar</a><a href="https://git.io/IndexNet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Zhang2020OrderlessReIDxxxarXiv.jpg"><b>Ordered or orderless: a revisit for video based person re-identification</b>
<br />\(\cdot\) <i>L. Zhang, Z. Shi, J. Zhou, M. Cheng, Y. Liu, J. Bian, Z. Zeng, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1912.11236" target=“blank”>arXiv</a><a href="https://doi.org/10.1109/TPAMI.2020.2976969" target=“blank”>link</a><a href="data/bibtex/Zhang2020OrderlessReID.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Ordered+or+Orderless:+A+Revisit+for+Video+based+Person+Re-Identification+Zhang,+Le+and+Shi,+Zenglin+and+Zhou,+Joey+Tianyi+and+Cheng,+Ming-Ming+and+Liu,+Yun+and+Bian,+Jia-Wang+and+Zeng,+Zeng+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Ordered+or+Orderless:+A+Revisit+for+Video+based+Person+Re-Identification" target=“blank”>semantic scholar</a><a href="https://github.com/ZhangLeUestc/VideoReid-TPAMI2020" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Plenty is plague: fine-grained learning for visual question answering</b>
<br />\(\cdot\) <i>Y. Zhou, R. Ji, J. Su, X. Sun, D. Meng, Y. Gao, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020</i>.
<br />\(\cdot\) <a href="https://doi.org/10.1109/TPAMI.2019.2956699" target=“blank”>link</a><a href="data/bibtex/Zhou2020TPAMIZhou.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Plenty+Is+Plague:+Fine-Grained+Learning+for+Visual+Question+Answering+Zhou,+Yiyi+and+Ji,+Rongrong+and+Su,+Jinsong+and+Sun,+Xiaoshuai+and+Meng,+Deyu+and+Gao,+Yue+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Plenty+Is+Plague:+Fine-Grained+Learning+for+Visual+Question+Answering" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1806.01576.pdf"><img class="imgP right" src="data/thumbnail/Adaptive2019ZhangxxxarXiv.jpg"></a><b>Adaptive importance learning for improving lightweight image super-resolution network</b>
<br />\(\cdot\) <i>L. Zhang, P. Wang, C. Shen, L. Liu, W. Wei, Y. Zhang, A. van den Hengel</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2019</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1806.01576" target=“blank”>arXiv</a><a href="data/bibtex/Adaptive2019Zhang.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Adaptive+Importance+Learning+for+Improving+Lightweight+Image+Super-resolution+Network+Zhang,+Lei+and+Wang,+Peng+and+Shen,+Chunhua+and+Liu,+Lingqiao+and+Wei,+Wei+and+Zhang,+Yanning+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Adaptive+Importance+Learning+for+Improving+Lightweight+Image+Super-resolution+Network" target=“blank”>semantic scholar</a><a href="https://tinyurl.com/Super-resolution-Network" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>RefineNet: multi-path refinement networks for dense prediction</b>
<br />\(\cdot\) <i>G. Lin, F. Liu, A. Milan, C. Shen, I. Reid</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019</i>.
<br />\(\cdot\) <a href="https://doi.org/10.1109/TPAMI.2019.2893630" target=“blank”>link</a><a href="data/bibtex/Fayao2019PAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={RefineNet}:+Multi-Path+Refinement+Networks+for+Dense+Prediction+Lin,+Guosheng+and+Liu,+Fayao+and+Milan,+Anton+and+Shen,+Chunhua+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={RefineNet}:+Multi-Path+Refinement+Networks+for+Dense+Prediction" target=“blank”>semantic scholar</a><a href="https://github.com/guosheng/refinenet" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Pytorch code is <a href="https://github.com/DrSleep/refinenet-pytorch" target=“blank”>here</a>.
</p>
</li></ol>
</li>
<li><p><b>Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction</b>
<br />\(\cdot\) <i>L. Zhang, W. Wei, Y. Zhang, C. Shen, A. van den Hengel, Q. Shi</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2018</i>.
<br />\(\cdot\) <a href="https://www.researchgate.net/publication/323914969_Cluster_Sparsity_Field_An_Internal_Hyperspectral_Imagery_Prior_for_Reconstruction" target=“blank”>pdf</a><a href="data/bibtex/Zhang2018IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Cluster+Sparsity+Field:+An+Internal+Hyperspectral+Imagery+Prior+for+Reconstruction+Zhang,+Lei+and+Wei,+Wei+and+Zhang,+Yanning+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton+and+Shi,+Qinfeng" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Cluster+Sparsity+Field:+An+Internal+Hyperspectral+Imagery+Prior+for+Reconstruction" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Ordinal constraint binary coding for approximate nearest neighbor search</b>
<br />\(\cdot\) <i>H. Liu, R. Ji, J. Wang, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018</i>.
<br />\(\cdot\) <a href="https://www.researchgate.net/publication/324053386_Ordinal_Constraint_Binary_Coding_for_Approximate_Nearest_Neighbor_Search" target=“blank”>pdf</a><a href="data/bibtex/HLiu2018TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Ordinal+Constraint+Binary+Coding+for+Approximate+Nearest+Neighbor+Search+Liu,+Hong+and+Ji,+Rongrong+and+Wang,+Jingdong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Ordinal+Constraint+Binary+Coding+for+Approximate+Nearest+Neighbor+Search" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Wang2017FVQAxxxarXiv.jpg"><b>FVQA: fact-based visual question answering</b>
<br />\(\cdot\) <i>P. Wang, Q. Wu, C. Shen, A. Dick, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1606.05433" target=“blank”>arXiv</a><a href="data/bibtex/Wang2017FVQA.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FVQA}:+Fact-based+Visual+Question+Answering+Wang,+Peng+and+Wu,+Qi+and+Shen,+Chunhua+and+Dick,+Anthony+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FVQA}:+Fact-based+Visual+Question+Answering" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/IJCV2017LinxxxarXiv.jpg"><b>Structured learning of binary codes with column generation for optimizing ranking measures</b>
<br />\(\cdot\) <i>G. Lin, F. Liu, C. Shen, J. Wu, H. Shen</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2017</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1602.06654" target=“blank”>arXiv</a><a href="data/bibtex/IJCV2017Lin.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Structured+Learning+of+Binary+Codes+with+Column+Generation+for+Optimizing+Ranking+Measures+Lin,+Guosheng+and+Liu,+Fayao+and+Shen,+Chunhua+and+Wu,+Jianxin+and+Shen,+Heng+Tao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Structured+Learning+of+Binary+Codes+with+Column+Generation+for+Optimizing+Ranking+Measures" target=“blank”>semantic scholar</a><a href="https://bitbucket.org/guosheng/structhash" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Lin2017SemanticxxxarXiv.jpg"><b>Exploring context with deep structured models for semantic segmentation</b>
<br />\(\cdot\) <i>G. Lin, C. Shen, A. van den Hengel, I. Reid</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1603.03183" target=“blank”>arXiv</a><a href="data/bibtex/Lin2017Semantic.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Exploring+Context+with+Deep+Structured+models+for+Semantic+Segmentation+Lin,+Guosheng+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Exploring+Context+with+Deep+Structured+models+for+Semantic+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1510.00921.pdf"><img class="imgP right" src="data/thumbnail/Cross2017LiuxxxarXiv.jpg"></a><b>Cross-convolutional-layer pooling for image recognition</b>
<br />\(\cdot\) <i>L. Liu, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1510.00921" target=“blank”>arXiv</a><a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7779086" target=“blank”>link</a><a href="data/bibtex/Cross2017Liu.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Cross-convolutional-layer+Pooling+for+Image+Recognition+Liu,+Lingqiao+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Cross-convolutional-layer+Pooling+for+Image+Recognition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/TPAMI2017LiuxxxarXiv.jpg"><b>Compositional model based Fisher vector coding for image classification</b>
<br />\(\cdot\) <i>L. Liu, P. Wang, C. Shen, L. Wang, A. van den Hengel, C. Wang, H. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1601.04143" target=“blank”>arXiv</a><a href="data/bibtex/TPAMI2017Liu.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Compositional+Model+based+{F}isher+Vector+Coding+for+Image+Classification+Liu,+Lingqiao+and+Wang,+Peng+and+Shen,+Chunhua+and+Wang,+Lei+and+{van+den+Hengel},+Anton+and+Wang,+Chao+and+Shen,+Heng+Tao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Compositional+Model+based+{F}isher+Vector+Coding+for+Image+Classification" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Wu2017ExternalxxxarXiv.jpg"><b>Image captioning and visual question answering based on attributes and external knowledge</b>
<br />\(\cdot\) <i>Q. Wu, C. Shen, P. Wang, A. Dick, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1603.02814" target=“blank”>arXiv</a><a href="data/bibtex/Wu2017External.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Image+Captioning+and+Visual+Question+Answering+Based+on+Attributes+and+External+Knowledge+Wu,+Qi+and+Shen,+Chunhua+and+Wang,+Peng+and+Dick,+Anthony+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Image+Captioning+and+Visual+Question+Answering+Based+on+Attributes+and+External+Knowledge" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Yao2016IJCVxxxarXiv.jpg"><b>Mining mid-level visual patterns with deep CNN activations</b>
<br />\(\cdot\) <i>Y. Li, L. Liu, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1506.06343" target=“blank”>arXiv</a><a href="http://rdcu.be/j1mA" target=“blank”>link</a><a href="data/bibtex/Yao2016IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Mining+Mid-level+Visual+Patterns+with+Deep+{CNN}+Activations+Li,+Yao+and+Liu,+Lingqiao+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Mining+Mid-level+Visual+Patterns+with+Deep+{CNN}+Activations" target=“blank”>semantic scholar</a><a href="https://github.com/yaoliUoA/MDPM" target=“blank”>project webpage</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1404.5009.pdf"><img class="imgP right" src="data/thumbnail/BnB2015WangxxxarXiv.jpg"></a><b>Efficient semidefinite branch-and-cut for MAP-MRF inference</b>
<br />\(\cdot\) <i>P. Wang, C. Shen, A. van den Hengel, P. Torr</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1404.5009" target=“blank”>arXiv</a><a href="http://doi.org/10.1007/s11263-015-0865-2" target=“blank”>link</a><a href="data/bibtex/BnB2015Wang.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Efficient+Semidefinite+Branch-and-Cut+for+{MAP-MRF}+Inference+Wang,+Peng+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton+and+Torr,+Philip" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Efficient+Semidefinite+Branch-and-Cut+for+{MAP-MRF}+Inference" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Zhang2015IJCVxxxarXiv.jpg"><b>Unsupervised feature learning for dense correspondences across scenes</b>
<br />\(\cdot\) <i>C. Zhang, C. Shen, T. Shen</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1501.00642" target=“blank”>arXiv</a><a href="data/bibtex/Zhang2015IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Unsupervised+Feature+Learning+for+Dense+Correspondences+across+Scenes+Zhang,+Chao+and+Shen,+Chunhua+and+Shen,+Tingzhi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Unsupervised+Feature+Learning+for+Dense+Correspondences+across+Scenes" target=“blank”>semantic scholar</a><a href="https://bitbucket.org/chhshen/ufl" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Xi2015TPAMIxxxarXiv.jpg"><b>Online metric-weighted linear representations for robust visual tracking</b>
<br />\(\cdot\) <i>X. Li, C. Shen, A. Dick, Z. Zhang, Y. Zhuang</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1507.05737" target=“blank”>arXiv</a><a href="data/bibtex/Xi2015TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Online+Metric-Weighted+Linear+Representations+for+Robust+Visual+Tracking+Li,+Xi+and+Shen,+Chunhua+and+Dick,+Anthony+and+Zhang,+Zhongfei+and+Zhuang,+Yueting" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Online+Metric-Weighted+Linear+Representations+for+Robust+Visual+Tracking" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Depth2015LiuxxxarXiv.jpg"><b>Learning depth from single monocular images using deep convolutional neural fields</b>
<br />\(\cdot\) <i>F. Liu, C. Shen, G. Lin, I. Reid</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1502.07411" target=“blank”>arXiv</a><a href="http://dx.doi.org/10.1109/TPAMI.2015.2505283" target=“blank”>link</a><a href="data/bibtex/Depth2015Liu.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+Depth+from+Single+Monocular+Images+Using+Deep+Convolutional+Neural+Fields+Liu,+Fayao+and+Shen,+Chunhua+and+Lin,+Guosheng+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+Depth+from+Single+Monocular+Images+Using+Deep+Convolutional+Neural+Fields" target=“blank”>semantic scholar</a><a href="http://goo.gl/rAKWrS" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Liu2015TPAMIxxxarXiv.jpg"><b>A generalized probabilistic framework for compact codebook creation</b>
<br />\(\cdot\) <i>L. Liu, L. Wang, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1401.7713" target=“blank”>arXiv</a><a href="http://doi.org/10.1109/TPAMI.2015.2441069" target=“blank”>link</a><a href="data/bibtex/Liu2015TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=A+Generalized+Probabilistic+Framework+for+Compact+Codebook+Creation+Liu,+Lingqiao+and+Wang,+Lei+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=A+Generalized+Probabilistic+Framework+for+Compact+Codebook+Creation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Paisitkriangkrai2015TPAMIxxxarXiv.jpg"><b>Pedestrian detection with spatially pooled features and structured ensemble learning</b>
<br />\(\cdot\) <i>S. Paisitkriangkrai, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1409.5209" target=“blank”>arXiv</a><a href="http://doi.org/10.1109/TPAMI.2015.2474388" target=“blank”>link</a><a href="data/bibtex/Paisitkriangkrai2015TPAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Pedestrian+Detection+with+Spatially+Pooled+Features+and+Structured+Ensemble+Learning+Paisitkriangkrai,+Sakrapee+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Pedestrian+Detection+with+Spatially+Pooled+Features+and+Structured+Ensemble+Learning" target=“blank”>semantic scholar</a><a href="https://github.com/chhshen/pedestrian-detection" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Large-scale binary quadratic optimization using semidefinite relaxation and applications</b>
<br />\(\cdot\) <i>P. Wang, C. Shen, A. van den Hengel, P. Torr</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1411.7564" target=“blank”>arXiv</a><a href="http://dx.doi.org/10.1109/TPAMI.2016.2541146" target=“blank”>link</a><a href="data/bibtex/BQP2015Wang.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Large-scale+Binary+Quadratic+Optimization+Using+Semidefinite+Relaxation+and+Applications+Wang,+Peng+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton+and+Torr,+Philip+H.+S." target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Large-scale+Binary+Quadratic+Optimization+Using+Semidefinite+Relaxation+and+Applications" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1401.8126.pdf"><img class="imgP right" src="data/thumbnail/Harandi2015IJCVxxxarXiv.jpg"></a><b>Extrinsic methods for coding and dictionary learning on Grassmann manifolds</b>
<br />\(\cdot\) <i>M. Harandi, R. Hartley, C. Shen, B. Lovell, C. Sanderson</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2015</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1401.8126" target=“blank”>arXiv</a><a href="data/bibtex/Harandi2015IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Extrinsic+Methods+for+Coding+and+Dictionary+Learning+on+{G}rassmann+Manifolds+Harandi,+Mehrtash+and+Hartley,+Richard+and+Shen,+Chunhua+and+Lovell,+Brian+and+Sanderson,+Conrad" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Extrinsic+Methods+for+Coding+and+Dictionary+Learning+on+{G}rassmann+Manifolds" target=“blank”>semantic scholar</a><a href="https://github.com/chhshen/Grassmann/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1408.5574.pdf"><img class="imgP right" src="data/thumbnail/FastHash2015LinxxxarXiv.jpg"></a><b>Supervised hashing using graph cuts and boosted decision trees</b>
<br />\(\cdot\) <i>G. Lin, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1408.5574" target=“blank”>arXiv</a><a href="http://dx.doi.org/10.1109/TPAMI.2015.2404776" target=“blank”>link</a><a href="data/bibtex/FastHash2015Lin.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Supervised+Hashing+Using+Graph+Cuts+and+Boosted+Decision+Trees+Lin,+Guosheng+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Supervised+Hashing+Using+Graph+Cuts+and+Boosted+Decision+Trees" target=“blank”>semantic scholar</a><a href="https://bitbucket.org/chhshen/fasthash/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Shen2014SBoostingxxxarXiv.jpg"><b>StructBoost: Boosting methods for predicting structured output variables</b>
<br />\(\cdot\) <i>C. Shen, G. Lin, A. van den Hengel</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1302.3283" target=“blank”>arXiv</a><a href="http://dx.doi.org/10.1109/TPAMI.2014.2315792" target=“blank”>link</a><a href="http://goo.gl/goCVLK" target=“blank”>pdf</a><a href="data/bibtex/Shen2014SBoosting.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={StructBoost}:+{B}oosting+Methods+for+Predicting+Structured+Output+Variables+Shen,+Chunhua+and+Lin,+Guosheng+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={StructBoost}:+{B}oosting+Methods+for+Predicting+Structured+Output+Variables" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>A hierarchical word-merging algorithm with class separability measure</b>
<br />\(\cdot\) <i>L. Wang, L. Zhou, C. Shen, L. Liu, H. Liu</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014</i>.
<br />\(\cdot\) <a href="https://bitbucket.org/chhshen/chhshen.bitbucket.org/src/be12d4ef8deb6207ec97f0fdac6efbe2df151b59/_download/TPAMI14Wang.pdf" target=“blank”>pdf</a><a href="data/bibtex/Wang2014PAMI.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=A+Hierarchical+Word-merging+Algorithm+with+Class+Separability+Measure+Wang,+Lei+and+Zhou,+Luping+and+Shen,+Chunhua+and+Liu,+Lingqiao+and+Liu,+Huan" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=A+Hierarchical+Word-merging+Algorithm+with+Class+Separability+Measure" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1301.2032.pdf"><img class="imgP right" src="data/thumbnail/FisherBoost2013IJCVxxxarXiv.jpg"></a><b>Training effective node classifiers for cascade classification</b>
<br />\(\cdot\) <i>C. Shen, P. Wang, S. Paisitkriangkrai, A. van den Hengel</i>.
<br />\(\cdot\) <i>International Journal of Computer Vision (IJCV), 2013</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1301.2032" target=“blank”>arXiv</a><a href="http://link.springer.com/article/10.1007%2Fs11263-013-0608-1" target=“blank”>link</a><a href="data/bibtex/FisherBoost2013IJCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Training+Effective+Node+Classifiers+for+Cascade+Classification+Shen,+Chunhua+and+Wang,+Peng+and+Paisitkriangkrai,+Sakrapee+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Training+Effective+Node+Classifiers+for+Cascade+Classification" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/TPAMI2013XixxxarXiv.jpg"><b>Incremental learning of 3D-DCT compact representations for robust visual tracking</b>
<br />\(\cdot\) <i>X. Li, A. Dick, C. Shen, A. van den Hengel, H. Wang</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1207.3389" target=“blank”>arXiv</a><a href="http://dx.doi.org/10.1109/TPAMI.2012.166" target=“blank”>link</a><a href="https://sites.google.com/site/chhshen/publication/tpami12xi.pdf?attredirects=1" target=“blank”>pdf</a><a href="data/bibtex/TPAMI2013Xi.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Incremental+Learning+of+{3D-DCT}+Compact+Representations+for+Robust+Visual+Tracking+Li,+Xi+and+Dick,+Anthony+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton+and+Wang,+Hanzi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Incremental+Learning+of+{3D-DCT}+Compact+Representations+for+Robust+Visual+Tracking" target=“blank”>semantic scholar</a><a href="https://github.com/chhshen/DCT-Tracking/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>UBoost: Boosting with the Universum</b>
<br />\(\cdot\) <i>C. Shen, P. Wang, F. Shen, H. Wang</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2012</i>.
<br />\(\cdot\) <a href="http://hdl.handle.net/2440/67027" target=“blank”>pdf</a><a href="data/bibtex/UBoost2011Shen.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={UBoost}:+{B}oosting+with+the+{U}niversum+Shen,+Chunhua+and+Wang,+Peng+and+Shen,+Fumin+and+Wang,+Hanzi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={UBoost}:+{B}oosting+with+the+{U}niversum" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Dual2010ShenxxxarXiv.jpg"><b>On the dual formulation of boosting algorithms</b>
<br />\(\cdot\) <i>C. Shen, H. Li</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2010</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/0901.3590" target=“blank”>arXiv</a><a href="http://dx.doi.org/10.1109/TPAMI.2010.47" target=“blank”>link</a><a href="data/bibtex/Dual2010Shen.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=On+the+Dual+Formulation+of+Boosting+Algorithms+Shen,+Chunhua+and+Li,+Hanxi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=On+the+Dual+Formulation+of+Boosting+Algorithms" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Adaptive object tracking based on an effective appearance filter</b>
<br />\(\cdot\) <i>H. Wang, D. Suter, K. Schindler, C. Shen</i>.
<br />\(\cdot\) <i>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2007</i>.
<br />\(\cdot\) <a href="http://dx.doi.org/10.1109/TPAMI.2007.1112" target=“blank”>link</a><a href="http://goo.gl/6rQTA1" target=“blank”>pdf</a><a href="data/bibtex/Adaptive2007Wang.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Adaptive+object+tracking+based+on+an+effective+appearance+filter+Wang,+Hanzi+and+Suter,+David+and+Schindler,+Konrad+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Adaptive+object+tracking+based+on+an+effective+appearance+filter" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Featured article of September issue 2007.
</p>
</li>
</ol>
</li>
</ol>
<h2>Conference: 182</h2>
<h3><a href="https://dblp.org/search/publ?q=author%3AChunhua_Shen%3Avenue%3ACVPR" target=“blank”>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</a>: 93</h3>
<h3><a href="https://dblp.org/search/publ?q=author%3AChunhua_Shen%3Avenue%3AICCV" target=“blank”>Proc. IEEE International Conference on Computer Vision (ICCV)</a>: 34</h3>
<h3><a href="https://dblp.org/search/publ?q=author%3AChunhua_Shen%3Avenue%3AECCV" target=“blank”>Proc. European Conference on Computer Vision (ECCV)</a>: 26</h3>
<h3><a href="https://dblp.org/search/publ?q=author:Chunhua_Shen:venue:ICML" target=“blank”>Proc. International Conference on Machine Learning (ICML)</a>: 6</h3>
<h3><a href="https://dblp.org/search/publ?q=author%3AChunhua_Shen%3Avenue%3AICLR" target=“blank”>Proc. International Conference on Learning Represenations (ICLR)</a>: 4</h3>
<h3><a href="https://papers.nips.cc/papers/search?q=chunhua+shen" target=“blank”>Proc. Advances in Neural Information Processing Systems (NeurIPS)</a>: 19</h3>
<p><br />
</p>
<ol reversed>
<li><p><b>FreeCustom: tuning-free customized image generation for multi-concept composition</b>
<br />\(\cdot\) <i>G. Ding, C. Zhao, W. Wang, Z. Yang, Z. Liu, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/DingCVPR2024.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FreeCustom}:+Tuning-Free+Customized+Image+Generation+for+Multi-Concept+Composition+Ding,+Ganggui+and+Zhao,+Canyu+and+Wang,+Wen+and+Yang,+Zhen+and+Liu,+Zide+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FreeCustom}:+Tuning-Free+Customized+Image+Generation+for+Multi-Concept+Composition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>DiverGen: improving instance segmentation by learning wider data distribution with more diverse generative data</b>
<br />\(\cdot\) <i>C. Fan, M. Zhu, H. Chen, Y. Liu, W. Wu, H. Zhang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/FanCVPR2024.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DiverGen}:+Improving+Instance+Segmentation+by+Learning+Wider+Data+Distribution+with+More+Diverse+Generative+Data+Fan,+Chengxiang+and+Zhu,+Muzhi+and+Chen,+Hao+and+Liu,+Yang+and+Wu,+Weijia+and+Zhang,+Huaqi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DiverGen}:+Improving+Instance+Segmentation+by+Learning+Wider+Data+Distribution+with+More+Diverse+Generative+Data" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Traffic scene parsing through the tsp6k dataset</b>
<br />\(\cdot\) <i>P. Jiang, Y. Yang, Y. Cao, Q. Hou, M. Cheng, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/JiangCVPR2024.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Traffic+Scene+Parsing+through+the+TSP6K+Dataset+Jiang,+Peng-Tao+and+Yang,+Yuqi+and+Cao,+Yang+and+Hou,+Qibin+and+Cheng,+Ming-Ming+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Traffic+Scene+Parsing+through+the+TSP6K+Dataset" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>FreeCompose: generic zero-shot image composition with diffusion prior</b>
<br />\(\cdot\) <i>Z. Chen, W. Wang, Z. Yang, Z. Yuan, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/FreeCompose2024ECCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FreeCompose}:+Generic+Zero-Shot+Image+Composition+with+Diffusion+Prior+Chen,+Zhekai+and+Wang,+Wen+and+Yang,+Zhen+and+Yuan,+Zeqing+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FreeCompose}:+Generic+Zero-Shot+Image+Composition+with+Diffusion+Prior" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/FreeCompose" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>VisionLLaMA: a unified llama backbone for vision tasks</b>
<br />\(\cdot\) <i>X. Chu, J. Su, B. Zhang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/VisionLLAMA2024ECCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={VisionLLaMA}:+A+Unified+LLaMA+Backbone+for+Vision+Tasks+Chu,+Xiangxiang+and+Su,+Jianlin+and+Zhang,+Bo+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={VisionLLaMA}:+A+Unified+LLaMA+Backbone+for+Vision+Tasks" target=“blank”>semantic scholar</a><a href="https://github.com/Meituan-AutoML/VisionLLaMA" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Matcher: segment anything with one shot using all-purpose feature matching</b>
<br />\(\cdot\) <i>Y. Liu, M. Zhu, H. Li, H. Chen, X. Wang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. International Conference on Learning Representations (ICLR’24), 2024</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2305.13310" target=“blank”>arXiv</a><a href="https://openreview.net/forum?id=yzRXdhk2he" target=“blank”>link</a><a href="data/bibtex/Liu2024ICLR.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Matcher:+Segment+Anything+with+One+Shot+Using+All-Purpose+Feature+Matching+Liu,+Yang+and+Zhu,+Muzhi+and+Li,+Hengtao+and+Chen,+Hao+and+Wang,+Xinlong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Matcher:+Segment+Anything+with+One+Shot+Using+All-Purpose+Feature+Matching" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>De novo protein design using geometric vector field networks</b>
<br />\(\cdot\) <i>W. Mao, M. Zhu, Z. Sun, S. Shen, L. Wu, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. International Conference on Learning Representations (ICLR’24), 2024</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2310.11802" target=“blank”>arXiv</a><a href="https://openreview.net/forum?id=9UIGyJJpay" target=“blank”>reviews</a><a href="data/bibtex/Mao2024ICLR.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=De+novo+protein+design+using+geometric+vector+field+networks+Mao,+Weian+and+Zhu,+Muzhi+and+Sun,+Zheng+and+Shen,+Shuaike+and+Wu,+Lin+Yuanbo+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=De+novo+protein+design+using+geometric+vector+field+networks" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Spotlight presentation.
</p>
</li></ol>
</li>
<li><p><b>Object-aware inversion and reassembly for image editing</b>
<br />\(\cdot\) <i>Z. Yang, D. Gui, W. Wang, H. Chen, B. Zhuang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. International Conference on Learning Representations (ICLR’24), 2024</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2310.12149" target=“blank”>arXiv</a><a href="https://openreview.net/forum?id=dpcVXiMlcv" target=“blank”>link</a><a href="data/bibtex/Yang2024ICLR.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Object-aware+Inversion+and+Reassembly+for+Image+Editing+Yang,+Zhen+and+Gui,+Dinggang+and+Wang,+Wen+and+Chen,+Hao+and+Zhuang,+Bohan+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Object-aware+Inversion+and+Reassembly+for+Image+Editing" target=“blank”>semantic scholar</a><a href="https://aim-uofa.github.io/OIR-Diffusion/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>On the trajectory regularity of ODE-based diffusion sampling</b>
<br />\(\cdot\) <i>D. Chen, Z. Zhou, C. Wang, C. Shen, S. Lyu</i>.
<br />\(\cdot\) <i>Proc. International Conference on Machine Learning (ICML’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Chen2024ICML.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=On+the+Trajectory+Regularity+of+{ODE}-based+Diffusion+Sampling+Chen,+Defang+and+Zhou,+Zhenyu+and+Wang,+Can+and+Shen,+Chunhua+and+Lyu,+Siwei" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=On+the+Trajectory+Regularity+of+{ODE}-based+Diffusion+Sampling" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Floating anchor diffusion model for multi-motif scaffolding</b>
<br />\(\cdot\) <i>K. Liu, S. Shen, W. Mao, X. Jiao, Z. Sun, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. International Conference on Machine Learning (ICML’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Liu2024ICML.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Floating+Anchor+Diffusion+Model+for+Multi-motif+Scaffolding+Liu,+Ke+and+Shen,+Shuaike+and+Mao,+Weian+and+Jiao,+Xiaoran+and+Sun,+Zheng+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Floating+Anchor+Diffusion+Model+for+Multi-motif+Scaffolding" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Generative active learning for long-tailed instance segmentation</b>
<br />\(\cdot\) <i>M. Zhu, C. Fan, H. Chen, Y. Liu, W. Mao, X. Xu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. International Conference on Machine Learning (ICML’24), 2024</i>.
<br />\(\cdot\) <a href="data/bibtex/Zhu2024ICML.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Generative+Active+Learning+for+Long-tailed+Instance+Segmentation+Zhu,+Muzhi+and+Fan,+Chengxiang+and+Chen,+Hao+and+Liu,+Yang+and+Mao,+Weian+and+Xu,+Xiaogang+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Generative+Active+Learning+for+Long-tailed+Instance+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Learning conditional attributes for compositional zero-shot learning</b>
<br />\(\cdot\) <i>Q. Wang, L. Liu, C. Jing, H. Chen, G. Liang, P. Wang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’23), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/CVPR2023B.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+Conditional+Attributes+for+Compositional+Zero-Shot+Learning+Wang,+Qingsheng+and+Liu,+Lingqiao+and+Jing,+Chenchen+and+Chen,+Hao+and+Liang,+Guoqiang+and+Wang,+Peng+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+Conditional+Attributes+for+Compositional+Zero-Shot+Learning" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/CVPR2023aWangxxxarXiv.jpg"><b>Images speak in images: a generalist painter for in-context visual learning</b>
<br />\(\cdot\) <i>X. Wang, W. Wang, Y. Cao, C. Shen, T. Huang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2212.02499" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2023aWang.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Images+Speak+in+Images:+A+Generalist+Painter+for+In-Context+Visual+Learning+Wang,+Xinlong+and+Wang,+Wen+and+Cao,+Yue+and+Shen,+Chunhua+and+Huang,+Tiejun" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Images+Speak+in+Images:+A+Generalist+Painter+for+In-Context+Visual+Learning" target=“blank”>semantic scholar</a><a href="https://github.com/baaivision/Painter" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Zolly: zoom focal length correctly for perspective-distorted human mesh reconstruction</b>
<br />\(\cdot\) <i>W. Wang, Y. Ge, H. Mei, Z. Cai, Q. Sun, C. Shen, Y. Wang, L. Yang, T. Komura</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/WangW2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Zolly:+Zoom+Focal+Length+Correctly+for+Perspective-Distorted+Human+Mesh+Reconstruction+Wang,+Wenjia+and+Ge,+Yongtao+and+Mei,+Haiyi+and+Cai,+Zhongang+and+Sun,+Qingping+and+Shen,+Chunhua+and+Wang,+Yanjun+and+Yang,+Lei+and+Komura,+Taku" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Zolly:+Zoom+Focal+Length+Correctly+for+Perspective-Distorted+Human+Mesh+Reconstruction" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><img class="imgP right" src="data/thumbnail/SegGPT2023ICCVxxxarXiv.jpg"><b>SegGPT: towards segmenting everything in context</b>
<br />\(\cdot\) <i>X. Wang, X. Zhang, Y. Cao, W. Wang, C. Shen, T. Huang</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2304.03284" target=“blank”>arXiv</a><a href="data/bibtex/SegGPT2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SegGPT}:+Towards+Segmenting+Everything+In+Context+Wang,+Xinlong+and+Zhang,+Xiaosong+and+Cao,+Yue+and+Wang,+Wen+and+Shen,+Chunhua+and+Huang,+Tiejun" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SegGPT}:+Towards+Segmenting+Everything+In+Context" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/DiffuMask2023ICCVxxxarXiv.jpg"><b>Diffumask: synthesizing images with pixel-level annotations for semantic segmentation using diffusion models</b>
<br />\(\cdot\) <i>W. Wu, Y. Zhao, M. Shou, H. Zhou, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2303.11681" target=“blank”>arXiv</a><a href="data/bibtex/DiffuMask2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Diffumask:+Synthesizing+images+with+pixel-level+annotations+for+semantic+segmentation+using+diffusion+models+Wu,+Weijia+and+Zhao,+Yuzhong+and+Shou,+Mike+Zheng+and+Zhou,+Hong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Diffumask:+Synthesizing+images+with+pixel-level+annotations+for+semantic+segmentation+using+diffusion+models" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Pose-free 3d scene reconstruction with frozen depth models</b>
<br />\(\cdot\) <i>G. Xu, W. Yin, H. Chen, C. Shen, K. Cheng, F. Zhao</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/XuG2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Pose-free+3D+Scene+Reconstruction+with+Frozen+Depth+Models+Xu,+Guangkai+and+Yin,+Wei+and+Chen,+Hao+and+Shen,+Chunhua+and+Cheng,+Kai+and+Zhao,+Feng" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Pose-free+3D+Scene+Reconstruction+with+Frozen+Depth+Models" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Yinwei2023ICCVxxxarXiv.jpg"><b>Metric3D: towards zero-shot metric 3d prediction from a single image</b>
<br />\(\cdot\) <i>W. Yin, C. Zhang, H. Chen, Z. Cai, G. Yu, K. Wang, X. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2307.10984" target=“blank”>arXiv</a><a href="data/bibtex/Yinwei2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={Metric3D}:+Towards+Zero-shot+Metric+3D+Prediction+from+A+Single+Image+Yin,+Wei+and+Zhang,+Chi+and+Chen,+Hao+and+Cai,+Zhipeng+and+Yu,+Gang+and+Wang,+Kaixuan+and+Chen,+Xiaozhi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={Metric3D}:+Towards+Zero-shot+Metric+3D+Prediction+from+A+Single+Image" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/KYQZ2023ICCVxxxarXiv.jpg"><b>CTVIS: consistent training for online video instance segmentation</b>
<br />\(\cdot\) <i>K. Ying, Q. Zhong, W. Mao, Z. Wang, H. Chen, L. Wu, Y. Liu, C. Fan, Y. Zhuge, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2307.12616" target=“blank”>arXiv</a><a href="data/bibtex/KYQZ2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={CTVIS}:+Consistent+Training+for+Online+Video+Instance+Segmentation+Ying,+Kaining+and+Zhong,+Qing+and+Mao,+Weian+and+Wang,+Zhenhua+and+Chen,+Hao+and+Wu,+Lin+Yuanbo+and+Liu,+Yifan+and+Fan,+Chengxiang+and+Zhuge,+Yunzhi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={CTVIS}:+Consistent+Training+for+Online+Video+Instance+Segmentation" target=“blank”>semantic scholar</a><a href="https://github.com/KainingYing/CTVIS" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Robust geometry-preserving depth estimation using differentiable rendering</b>
<br />\(\cdot\) <i>C. Zhang, W. Yin, G. Yu, Z. Wang, T. Chen, B. Fu, J. Zhou, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/ZhangC2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Robust+Geometry-Preserving+Depth+Estimation+Using+Differentiable+Rendering+Zhang,+Chi+and+Yin,+Wei+and+Yu,+Gang+and+Wang,+Zhibin+and+Chen,+Tao+and+Fu,+Bin+and+Zhou,+Joey+Tianyi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Robust+Geometry-Preserving+Depth+Estimation+Using+Differentiable+Rendering" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/YZhao2023ICCVxxxarXiv.jpg"><b>Generative prompt model for weakly supervised object localization</b>
<br />\(\cdot\) <i>Y. Zhao, Q. Ye, W. Wu, C. Shen, F. Wan</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2307.09756" target=“blank”>arXiv</a><a href="data/bibtex/YZhao2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Generative+Prompt+Model+for+Weakly+Supervised+Object+Localization+Zhao,+Yuzhong+and+Ye,+Qixiang+and+Wu,+Weijia+and+Shen,+Chunhua+and+Wan,+Fang" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Generative+Prompt+Model+for+Weakly+Supervised+Object+Localization" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Segprompt: boosting open-world segmentation via category-level prompt learning</b>
<br />\(\cdot\) <i>M. Zhu, H. Li, H. Chen, C. Fan, W. Mao, C. Jing, Y. Liu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023</i>.
<br />\(\cdot\) <a href="data/bibtex/Zhu2023ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=SegPrompt:+Boosting+Open-World+Segmentation+via+Category-level+Prompt+Learning+Zhu,+Muzhi+and+Li,+Hengtao+and+Chen,+Hao+and+Fan,+Chengxiang+and+Mao,+Weian+and+Jing,+Chenchen+and+Liu,+Yifan+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=SegPrompt:+Boosting+Open-World+Segmentation+via+Category-level+Prompt+Learning" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/CPE2023ICLRxxxarXiv.jpg"><b>Conditional positional encodings for vision transformers</b>
<br />\(\cdot\) <i>X. Chu, Z. Tian, B. Zhang, X. Wang, X. Wei, H. Xia, C. Shen</i>.
<br />\(\cdot\) <i>Proc. International Conference on Learning Representations (ICLR’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2102.10882" target=“blank”>arXiv</a><a href="data/bibtex/CPE2023ICLR.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Conditional+Positional+Encodings+for+Vision+Transformers+Chu,+Xiangxiang+and+Tian,+Zhi+and+Zhang,+Bo+and+Wang,+Xinlong+and+Wei,+Xiaolin+and+Xia,+Huaxia+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Conditional+Positional+Encodings+for+Vision+Transformers" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Weijia2023DDMxxxarXiv.jpg"><b>DatasetDM: synthesizing data with perception annotations using diffusion models</b>
<br />\(\cdot\) <i>W. Wu, Y. Zhao, H. Chen, Y. Gu, R. Zhao, Y. He, H. Zhou, M. Shou, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’23), 2023</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2308.06160" target=“blank”>arXiv</a><a href="data/bibtex/Weijia2023DDM.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DatasetDM}:+Synthesizing+Data+with+Perception+Annotations+Using+Diffusion+Models+Wu,+Weijia+and+Zhao,+Yuzhong+and+Chen,+Hao+and+Gu,+Yuchao+and+Zhao,+Rui+and+He,+Yefei+and+Zhou,+Hong+and+Shou,+Mike+Zheng+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DatasetDM}:+Synthesizing+Data+with+Perception+Annotations+Using+Diffusion+Models" target=“blank”>semantic scholar</a><a href="https://weijiawu.github.io/DatasetDM_page/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Dai2022MattingxxxarXiv.jpg"><b>Boosting robustness of image matting with context assembling and strong data augmentation</b>
<br />\(\cdot\) <i>Y. Dai, B. Price, H. Zhang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2201.06889" target=“blank”>arXiv</a><a href="data/bibtex/Dai2022Matting.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Boosting+Robustness+of+Image+Matting+with+Context+Assembling+and+Strong+Data+Augmentation+Dai,+Yutong+and+Price,+Brian+and+Zhang,+He+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Boosting+Robustness+of+Image+Matting+with+Context+Assembling+and+Strong+Data+Augmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Catching both gray and black swans: open-set supervised anomaly detection</b>
<br />\(\cdot\) <i>C. Ding, G. Pan, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Ding2022Catching.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Catching+Both+Gray+and+Black+Swans:+Open-set+Supervised+Anomaly+Detection+Ding,+Choubo+and+Pan,+Guansong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Catching+Both+Gray+and+Black+Swans:+Open-set+Supervised+Anomaly+Detection" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>RigidFlow: self-supervised scene flow learning on point clouds by local rigidity prior</b>
<br />\(\cdot\) <i>R. Li, C. Zhang, G. Lin, Z. Wang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Li2022Rigid.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={RigidFlow}:+Self-Supervised+Scene+Flow+Learning+on+Point+Clouds+by+Local+Rigidity+Prior+Li,+Ruibo+and+Zhang,+Chi+and+Lin,+Guosheng+and+Wang,+Zhe+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={RigidFlow}:+Self-Supervised+Scene+Flow+Learning+on+Point+Clouds+by+Local+Rigidity+Prior" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Long2022RACxxxarXiv.jpg"><b>Retrieval augmented classification for long-tail visual recognition</b>
<br />\(\cdot\) <i>A. Long, W. Yin, T. Ajanthan, V. Nguyen, P. Purkait, R. Garg, A. Blair, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2202.11233" target=“blank”>arXiv</a><a href="data/bibtex/Long2022RAC.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Retrieval+Augmented+Classification+for+Long-Tail+Visual+Recognition+Long,+Alexander+and+Yin,+Wei+and+Ajanthan,+Thalaiyasingam+and+Nguyen,+Vu+and+Purkait,+Pulak+and+Garg,+Ravi+and+Blair,+Alan+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Retrieval+Augmented+Classification+for+Long-Tail+Visual+Recognition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Wang2022SoloxxxarXiv.jpg"><b>FreeSOLO: learning to segment objects without annotations</b>
<br />\(\cdot\) <i>X. Wang, Z. Yu, S. De Mello, J. Kautz, A. Anandkumar, C. Shen, J. Alvarez</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2202.12181" target=“blank”>arXiv</a><a href="data/bibtex/Wang2022Solo.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FreeSOLO}:+Learning+to+Segment+Objects+without+Annotations+Wang,+Xinlong+and+Yu,+Zhiding+and+{De+Mello},+Shalini+and+Kautz,+Jan+and+Anandkumar,+Anima+and+Shen,+Chunhua+and+Alvarez,+Jose" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FreeSOLO}:+Learning+to+Segment+Objects+without+Annotations" target=“blank”>semantic scholar</a><a href="https://git.io/AdelaiDet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>TopFormer: token pyramid transformer for mobile semantic segmentation</b>
<br />\(\cdot\) <i>W. Zhang, Z. Huang, G. Yu, T. Chen, G. Luo, X. Wang, W. Liu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Topformer2022.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={TopFormer}:+Token+Pyramid+Transformer+for+Mobile+Semantic+Segmentation+Zhang,+Wenqiang+and+Huang,+Zilong+and+Yu,+Gang+and+Chen,+Tao+and+Luo,+Guozhong+and+Wang,+Xinggang+and+Liu,+Wenyu+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={TopFormer}:+Token+Pyramid+Transformer+for+Mobile+Semantic+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Peixian2022DetectionxxxarXiv.jpg"><b>Efficient decoder-free object detection with transformers</b>
<br />\(\cdot\) <i>P. Chen, M. Zhang, Y. Shen, K. Sheng, Y. Gao, X. Sun, K. Li, C. Shen</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’22), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2206.06829" target=“blank”>arXiv</a><a href="data/bibtex/Peixian2022Detection.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Efficient+Decoder-free+Object+Detection+with+Transformers+Chen,+Peixian+and+Zhang,+Mengdan+and+Shen,+Yunhang+and+Sheng,+Kekai+and+Gao,+Yuting+and+Sun,+Xing+and+Li,+Ke+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Efficient+Decoder-free+Object+Detection+with+Transformers" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>DisCo: remedying self-supervised learning on lightweight models with distilled contrastive learning</b>
<br />\(\cdot\) <i>Y. Gao, J. Zhuang, S. Lin, H. Cheng, X. Sun, K. Li, C. Shen</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Gao2022DisCO.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DisCo}:+Remedying+Self-supervised+Learning+on+Lightweight+Models+with+Distilled+Contrastive+Learning+Gao,+Yuting+and+Zhuang,+Jia-Xin+and+Lin,+Shaohui+and+Cheng,+Hao+and+Sun,+Xing+and+Li,+Ke+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DisCo}:+Remedying+Self-supervised+Learning+on+Lightweight+Models+with+Distilled+Contrastive+Learning" target=“blank”>semantic scholar</a><a href="https://github.com/Yuting-Gao/DisCo-pytorch" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Tong2022DetectionxxxarXiv.jpg"><b>PointInst3D: segmenting 3D instances by points</b>
<br />\(\cdot\) <i>T. He, W. Yin, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’22), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2204.11402" target=“blank”>arXiv</a><a href="data/bibtex/Tong2022Detection.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={PointInst3D}:+Segmenting+{3D}+Instances+by+Points+He,+Tong+and+Yin,+Wei+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={PointInst3D}:+Segmenting+{3D}+Instances+by+Points" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Poseur2022DetectionxxxarXiv.jpg"><b>Poseur: direct human pose regression with transformers</b>
<br />\(\cdot\) <i>W. Mao, Y. Ge, C. Shen, Z. Tian, X. Wang, Z. Wang, A. van den Hengel</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’22), 2022</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2201.07412" target=“blank”>arXiv</a><a href="data/bibtex/Poseur2022Detection.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={Poseur}:+Direct+Human+Pose+Regression+with+Transformers+Mao,+Weian+and+Ge,+Yongtao+and+Shen,+Chunhua+and+Tian,+Zhi+and+Wang,+Xinlong+and+Wang,+Zhibin+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={Poseur}:+Direct+Human+Pose+Regression+with+Transformers" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/Poseur" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>PyramidCLIP: hierarchical feature alignment for vision-language model pretraining</b>
<br />\(\cdot\) <i>Y. Gao, J. Liu, Z. Xu, J. Zhang, K. Li, R. Ji, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Gao2022CLIP.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={PyramidCLIP}:+Hierarchical+Feature+Alignment+for+Vision-language+Model+Pretraining+Gao,+Yuting+and+Liu,+Jinfeng+and+Xu,+Zihan+and+Zhang,+Jun+and+Li,+Ke+and+Ji,+Rongrong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={PyramidCLIP}:+Hierarchical+Feature+Alignment+for+Vision-language+Model+Pretraining" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Adv-attribute: inconspicuous and transferable adversarial attack on face recognition</b>
<br />\(\cdot\) <i>S. Jia, B. Yin, T. Yao, S. Ding, C. Shen, X. Yang, C. Ma</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Jia2022AdvA.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Adv-Attribute:+Inconspicuous+and+Transferable+Adversarial+Attack+on+Face+Recognition+Jia,+Shuai+and+Yin,+Bangjie+and+Yao,+Taiping+and+Ding,+Shouhong+and+Shen,+Chunhua+and+Yang,+Xiaokang+and+Ma,+Chao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Adv-Attribute:+Inconspicuous+and+Transferable+Adversarial+Attack+on+Face+Recognition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Multi-dataset training of transformers for robust action recognition</b>
<br />\(\cdot\) <i>J. Liang, E. Zhang, J. Zhang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Liang2022Action.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Multi-dataset+Training+of+Transformers+for+Robust+Action+Recognition+Liang,+Junwei+and+Zhang,+Enwei+and+Zhang,+Jun+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Multi-dataset+Training+of+Transformers+for+Robust+Action+Recognition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Text-adaptive multiple visual prototype matching for video-text retrieval</b>
<br />\(\cdot\) <i>C. Lin, A. Wu, J. Liang, J. Zhang, W. Ge, W. Zheng, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Lin2022TVRc.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Text-Adaptive+Multiple+Visual+Prototype+Matching+for+Video-Text+Retrieval+Lin,+Chengzhi+and+Wu,+Ancong+and+Liang,+Junwei+and+Zhang,+Jun+and+Ge,+Wenhang+and+Zheng,+Wei-Shi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Text-Adaptive+Multiple+Visual+Prototype+Matching+for+Video-Text+Retrieval" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Fully convolutional one-stage 3D object detection on LiDAR range images</b>
<br />\(\cdot\) <i>Z. Tian, X. Chu, X. Wang, X. Wei, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Tian2022FCOSLidar.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Fully+Convolutional+One-Stage+{3D}+Object+Detection+on+{LiDAR}+Range+Images+Tian,+Zhi+and+Chu,+Xiangxiang+and+Wang,+Xiaoming+and+Wei,+Xiaolin+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Fully+Convolutional+One-Stage+{3D}+Object+Detection+on+{LiDAR}+Range+Images" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>SegViT: semantic segmentation with plain vision transformers</b>
<br />\(\cdot\) <i>B. Zhang, Z. Tian, Q. Tang, X. Chu, X. Wei, C. Shen, Y. Liu</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Zhang2022ViTb.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={SegViT}:+Semantic+Segmentation+with+Plain+Vision+Transformers+Zhang,+Bowen+and+Tian,+Zhi+and+Tang,+Quan+and+Chu,+Xiangxiang+and+Wei,+Xiaolin+and+Shen,+Chunhua+and+Liu,+Yifan" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={SegViT}:+Semantic+Segmentation+with+Plain+Vision+Transformers" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Hierarchical normalization for robust monocular depth estimation</b>
<br />\(\cdot\) <i>C. Zhang, W. Yin, Z. Wang, G. Yu, B. Fu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Chi2022Depth.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Hierarchical+Normalization+for+Robust+Monocular+Depth+Estimation+Zhang,+Chi+and+Yin,+Wei+and+Wang,+Zhibin+and+Yu,+Gang+and+Fu,+Bin+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Hierarchical+Normalization+for+Robust+Monocular+Depth+Estimation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>DENSE: data-free one-shot federated learning</b>
<br />\(\cdot\) <i>J. Zhang, C. Chen, B. Li, L. Lyu, S. Wu, S. Ding, C. Shen, C. Wu</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022</i>.
<br />\(\cdot\) <a href="data/bibtex/Zhang2022DENSE.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DENSE}:+Data-Free+One-Shot+Federated+Learning+Zhang,+Jie+and+Chen,+Chen+and+Li,+Bo+and+Lyu,+Lingjuan+and+Wu,+Shuang+and+Ding,+Shouhong+and+Shen,+Chunhua+and+Wu,+Chao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DENSE}:+Data-Free+One-Shot+Federated+Learning" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2007.06919.pdf"><img class="imgP right" src="data/thumbnail/Chen2021CVPR3xxxarXiv.jpg"></a><b>AQD: towards accurate quantized object detection</b>
<br />\(\cdot\) <i>P. Chen, J. Liu, B. Zhuang, M. Tan, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2007.06919" target=“blank”>arXiv</a><a href="data/bibtex/Chen2021CVPR3.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={AQD}:+Towards+Accurate+Quantized+Object+Detection+Chen,+Peng+and+Liu,+Jing+and+Zhuang,+Bohan+and+Tan,+Mingkui+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={AQD}:+Towards+Accurate+Quantized+Object+Detection" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2011.14288.pdf"><img class="imgP right" src="data/thumbnail/Dai2021CVPR8xxxarXiv.jpg"></a><b>Learning affinity-aware upsampling for deep image matting</b>
<br />\(\cdot\) <i>Y. Dai, H. Lu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.14288" target=“blank”>arXiv</a><a href="data/bibtex/Dai2021CVPR8.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+Affinity-Aware+Upsampling+for+Deep+Image+Matting+Dai,+Yutong+and+Lu,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+Affinity-Aware+Upsampling+for+Deep+Image+Matting" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2011.11204.pdf"><img class="imgP right" src="data/thumbnail/Guo2021CVPR14xxxarXiv.jpg"></a><b>Graph attention tracking</b>
<br />\(\cdot\) <i>D. Guo, Y. Shao, Y. Cui, Z. Wang, L. Zhang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.11204" target=“blank”>arXiv</a><a href="data/bibtex/Guo2021CVPR14.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Graph+Attention+Tracking+Guo,+Dongyan+and+Shao,+Yanyan+and+Cui,+Ying+and+Wang,+Zhenhua+and+Zhang,+Liyan+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Graph+Attention+Tracking" target=“blank”>semantic scholar</a><a href="https://git.io/SiamGAT" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/He2021CVPR7xxxarXiv.jpg"><b>DyCo3D: robust instance segmentation of 3d point clouds through dynamic convolution</b>
<br />\(\cdot\) <i>T. He, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.13328" target=“blank”>arXiv</a><a href="data/bibtex/He2021CVPR7.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DyCo3D}:+Robust+Instance+Segmentation+of+3D+Point+Clouds+through+Dynamic+Convolution+He,+Tong+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DyCo3D}:+Robust+Instance+Segmentation+of+3D+Point+Clouds+through+Dynamic+Convolution" target=“blank”>semantic scholar</a><a href="https://git.io/DyCo3D" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>HCRF-Flow: scene flow from point clouds with continuous high-order CRFs and position-aware flow embedding</b>
<br />\(\cdot\) <i>R. Li, G. Lin, T. He, F. Liu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Li2021CVPR12.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={HCRF-Flow}:+Scene+Flow+from+Point+Clouds+with+Continuous+High-order+{CRFs}+and+Position-aware+Flow+Embedding+Li,+Ruibo+and+Lin,+Guosheng+and+He,+Tong+and+Liu,+Fayao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={HCRF-Flow}:+Scene+Flow+from+Point+Clouds+with+Continuous+High-order+{CRFs}+and+Position-aware+Flow+Embedding" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Generic perceptual loss for modelling structured output dependencies</b>
<br />\(\cdot\) <i>Y. Liu, W. Yin, Y. Chen, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Liu2021CVPR5.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Generic+Perceptual+Loss+for+Modelling+Structured+Output+Dependencies+Liu,+Yifan+and+Yin,+Wei+and+Chen,+Yu+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Generic+Perceptual+Loss+for+Modelling+Structured+Output+Dependencies" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>FCPose: fully convolutional multi-person pose estimation with dynamic instance-aware convolutions</b>
<br />\(\cdot\) <i>W. Mao, Z. Tian, X. Wang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Mao2021CVPR4.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FCPose}:+Fully+Convolutional+Multi-Person+Pose+Estimation+with+Dynamic+Instance-Aware+Convolutions+Mao,+Weian+and+Tian,+Zhi+and+Wang,+Xinlong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FCPose}:+Fully+Convolutional+Multi-Person+Pose+Estimation+with+Dynamic+Instance-Aware+Convolutions" target=“blank”>semantic scholar</a><a href="https://git.io/AdelaiDet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><b>Feature decomposition and reconstruction learning for effective facial expression recognition</b>
<br />\(\cdot\) <i>D. Ruan, Y. Yan, S. Lai, Z. Chai, C. Shen, H. Wang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Ruan2021CVPR9.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Feature+Decomposition+and+Reconstruction+Learning+for+Effective+Facial+Expression+Recognition+Ruan,+Delian+and+Yan,+Yan+and+Lai,+Shenqi+and+Chai,+Zhenhua+and+Shen,+Chunhua+and+Wang,+Hanzi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Feature+Decomposition+and+Reconstruction+Learning+for+Effective+Facial+Expression+Recognition" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Learning spatial-semantic relationship for facial attribute recognition with limited labeled data</b>
<br />\(\cdot\) <i>Y. Shu, Y. Yan, S. Chen, J. Xue, C. Shen, H. Wang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Shu2021CVPR10.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+Spatial-Semantic+Relationship+for+Facial+Attribute+Recognition+with+Limited+Labeled+Data+Shu,+Ying+and+Yan,+Yan+and+Chen,+Si+and+Xue,+Jing-Hao+and+Shen,+Chunhua+and+Wang,+Hanzi" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+Spatial-Semantic+Relationship+for+Facial+Attribute+Recognition+with+Limited+Labeled+Data" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Tian2021CVPR2xxxarXiv.jpg"><b>BoxInst: high-performance instance segmentation with box annotations</b>
<br />\(\cdot\) <i>Z. Tian, C. Shen, X. Wang, H. Chen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2012.02310" target=“blank”>arXiv</a><a href="data/bibtex/Tian2021CVPR2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={BoxInst}:+High-Performance+Instance+Segmentation+with+Box+Annotations+Tian,+Zhi+and+Shen,+Chunhua+and+Wang,+Xinlong+and+Chen,+Hao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={BoxInst}:+High-Performance+Instance+Segmentation+with+Box+Annotations" target=“blank”>semantic scholar</a><a href="https://git.io/AdelaiDet" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Wang2021CVPR13xxxarXiv.jpg"><b>Dense contrastive learning for self-supervised visual pre-training</b>
<br />\(\cdot\) <i>X. Wang, R. Zhang, C. Shen, T. Kong, L. Li</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.09157" target=“blank”>arXiv</a><a href="data/bibtex/Wang2021CVPR13.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Dense+Contrastive+Learning+for+Self-Supervised+Visual+Pre-Training+Wang,+Xinlong+and+Zhang,+Rufeng+and+Shen,+Chunhua+and+Kong,+Tao+and+Li,+Lei" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Dense+Contrastive+Learning+for+Self-Supervised+Visual+Pre-Training" target=“blank”>semantic scholar</a><a href="https://git.io/AdelaiDet" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Wang2021CVPR11xxxarXiv.jpg"><b>End-to-end video instance segmentation with Transformers</b>
<br />\(\cdot\) <i>Y. Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, H. Xia</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.14503" target=“blank”>arXiv</a><a href="data/bibtex/Wang2021CVPR11.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=End-to-End+Video+Instance+Segmentation+with+{T}ransformers+Wang,+Yuqing+and+Xu,+Zhaoliang+and+Wang,+Xinlong+and+Shen,+Chunhua+and+Cheng,+Baoshan+and+Shen,+Hao+and+Xia,+Huaxia" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=End-to-End+Video+Instance+Segmentation+with+{T}ransformers" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Yin2021CVPR6xxxarXiv.jpg"><b>Learning to recover 3D scene shape from a single image</b>
<br />\(\cdot\) <i>W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2012.09365" target=“blank”>arXiv</a><a href="data/bibtex/Yin2021CVPR6.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+to+Recover+{3D}+Scene+Shape+from+a+Single+Image+Yin,+Wei+and+Zhang,+Jianming+and+Wang,+Oliver+and+Niklaus,+Simon+and+Mai,+Long+and+Chen,+Simon+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+to+Recover+{3D}+Scene+Shape+from+a+Single+Image" target=“blank”>semantic scholar</a><a href="https://git.io/Depth" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Listed as one of the Best Paper Candidates, 32 out of about 6000 submissions.
</p>
</li></ol>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Zhang2021CVPR1xxxarXiv.jpg"><b>DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets</b>
<br />\(\cdot\) <i>J. Zhang, Y. Xie, Y. Xia, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.10217" target=“blank”>arXiv</a><a href="data/bibtex/Zhang2021CVPR1.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DoDNet}:+Learning+to+segment+multi-organ+and+tumors+from+multiple+partially+labeled+datasets+Zhang,+Jianpeng+and+Xie,+Yutong+and+Xia,+Yong+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DoDNet}:+Learning+to+segment+multi-organ+and+tumors+from+multiple+partially+labeled+datasets" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/partially-labelled" target=“blank”>project webpage</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2008.05101.pdf"><img class="imgP right" src="data/thumbnail/Chen2021ICCVxxxarXiv.jpg"></a><b>FATNN: fast and accurate ternary neural networks</b>
<br />\(\cdot\) <i>P. Chen, B. Zhuang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2008.05101" target=“blank”>arXiv</a><a href="data/bibtex/Chen2021ICCV.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={FATNN}:+Fast+and+Accurate+Ternary+Neural+Networks+Chen,+Peng+and+Zhuang,+Bohan+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={FATNN}:+Fast+and+Accurate+Ternary+Neural+Networks" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Shu2021ICCVKDxxxarXiv.jpg"><b>Channel-wise knowledge distillation for dense prediction</b>
<br />\(\cdot\) <i>C. Shu, Y. Liu, J. Gao, L. Xu, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2011.13256" target=“blank”>arXiv</a><a href="data/bibtex/Shu2021ICCVKD.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Channel-wise+Knowledge+Distillation+for+Dense+Prediction+Shu,+Changyong+and+Liu,+Yifan+and+Gao,+Jianfei+and+Xu,+Lin+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Channel-wise+Knowledge+Distillation+for+Dense+Prediction" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Occluded person re-identification with single-scale global representations</b>
<br />\(\cdot\) <i>C. Yan, G. Pang, J. Jiao, X. Bai, X. Feng, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Yan2021ICCVOccl.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Occluded+Person+Re-Identification+with+Single-scale+Global+Representations+Yan,+Cheng+and+Pang,+Guansong+and+Jiao,+Jile+and+Bai,+Xiao+and+Feng,+Xuetao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Occluded+Person+Re-Identification+with+Single-scale+Global+Representations" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><b>BV-Person: a large-scale dataset for bird-view person re-identification</b>
<br />\(\cdot\) <i>C. Yan, G. Pang, L. Wang, J. Jiao, X. Feng, C. Shen, J. Li</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021</i>.
<br />\(\cdot\) <a href="data/bibtex/Yan2021ICCVBVPerson.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={BV-Person}:+A+Large-scale+Dataset+for+Bird-view+Person+Re-identification+Yan,+Cheng+and+Pang,+Guansong+and+Wang,+Lei+and+Jiao,+Jile+and+Feng,+Xuetao+and+Shen,+Chunhua+and+Li,+Jingjing" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={BV-Person}:+A+Large-scale+Dataset+for+Bird-view+Person+Re-identification" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Yuan2021ICCVSimplexxxarXiv.jpg"><b>A simple baseline for semi-supervised semantic segmentation with strong data augmentation</b>
<br />\(\cdot\) <i>J. Yuan, Y. Liu, C. Shen, Z. Wang, H. Li</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2104.07256" target=“blank”>arXiv</a><a href="data/bibtex/Yuan2021ICCVSimple.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=A+Simple+Baseline+for+Semi-supervised+Semantic+Segmentation+with+Strong+Data+Augmentation+Yuan,+Jianlong+and+Liu,+Yifan+and+Shen,+Chunhua+and+Wang,+Zhibin+and+Li,+Hao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=A+Simple+Baseline+for+Semi-supervised+Semantic+Segmentation+with+Strong+Data+Augmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Chizhang2021ICCVMetaxxxarXiv.jpg"><b>Meta navigator: search for a good adaptation policy for few-shot learning</b>
<br />\(\cdot\) <i>C. Zhang, H. Ding, G. Lin, R. Li, C. Wang, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2109.05749" target=“blank”>arXiv</a><a href="data/bibtex/Chizhang2021ICCVMeta.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Meta+Navigator:+Search+for+a+Good+Adaptation+Policy+for+Few-shot+Learning+Zhang,+Chi+and+Ding,+Henghui+and+Lin,+Guosheng+and+Li,+Ruibo+and+Wang,+Changhu+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Meta+Navigator:+Search+for+a+Good+Adaptation+Policy+for+Few-shot+Learning" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/Twins2021ChuxxxarXiv.jpg"><b>Twins: revisiting the design of spatial attention in vision transformers</b>
<br />\(\cdot\) <i>X. Chu, Z. Tian, Y. Wang, B. Zhang, H. Ren, X. Wei, H. Xia, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2104.13840" target=“blank”>arXiv</a><a href="data/bibtex/Twins2021Chu.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Twins:+Revisiting+the+Design+of+Spatial+Attention+in+Vision+Transformers+Chu,+Xiangxiang+and+Tian,+Zhi+and+Wang,+Yuqing+and+Zhang,+Bo+and+Ren,+Haibing+and+Wei,+Xiaolin+and+Xia,+Huaxia+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Twins:+Revisiting+the+Design+of+Spatial+Attention+in+Vision+Transformers" target=“blank”>semantic scholar</a><a href="https://github.com/Meituan-AutoML/Twins" target=“blank”>project webpage</a>
</p>
</li>
<li><p><img class="imgP right" src="data/thumbnail/DNRD2021ZhangxxxarXiv.jpg"><b>Dynamic neural representational decoders for high-resolution semantic segmentation</b>
<br />\(\cdot\) <i>B. Zhang, Y. Liu, Z. Tian, C. Shen</i>.
<br />\(\cdot\) <i>Proc. Advances in Neural Information Processing Systems (NeurIPS’21), 2021</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2107.14428" target=“blank”>arXiv</a><a href="data/bibtex/DNRD2021Zhang.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Dynamic+Neural+Representational+Decoders+for+High-Resolution+Semantic+Segmentation+Zhang,+Bowen+and+Liu,+Yifan+and+Tian,+Zhi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Dynamic+Neural+Representational+Decoders+for+High-Resolution+Semantic+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2001.00309.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020BlendMask7xxxarXiv.jpg"></a><b>BlendMask: top-down meets bottom-up for instance segmentation</b>
<br />\(\cdot\) <i>H. Chen, K. Sun, Z. Tian, C. Shen, Y. Huang, Y. Yan</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2001.00309" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020BlendMask7.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={BlendMask}:+Top-Down+Meets+Bottom-Up+for+Instance+Segmentation+Chen,+Hao+and+Sun,+Kunyang+and+Tian,+Zhi+and+Shen,+Chunhua+and+Huang,+Yongming+and+Yan,+Youliang" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={BlendMask}:+Top-Down+Meets+Bottom-Up+for+Instance+Segmentation" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/AdelaiDet/" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2002.10200.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Liu6xxxarXiv.jpg"></a><b>ABCNet: arbitrarily-shaped scene text spotting with adaptive Bezier-curve network in real time</b>
<br />\(\cdot\) <i>Y. Liu, H. Chen, C. Shen, T. He, L. Jin, L. Wang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2002.10200" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Liu6.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={ABCNet}:+Arbitrarily-Shaped+Scene+Text+Spotting+with+Adaptive+{B}ezier-Curve+Network+in+Real+Time+Liu,+Yuliang+and+Chen,+Hao+and+Shen,+Chunhua+and+He,+Tong+and+Jin,+Lianwen+and+Wang,+Liangwei" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={ABCNet}:+Arbitrarily-Shaped+Scene+Text+Spotting+with+Adaptive+{B}ezier-Curve+Network+in+Real+Time" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/AdelaiDet/" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2003.06780.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Self12xxxarXiv.jpg"></a><b>Self-trained deep ordinal regression for end-to-end video anomaly detection</b>
<br />\(\cdot\) <i>G. Pang, C. Yan, C. Shen, A. van den Hengel, X. Bai</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2003.06780" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Self12.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Self-trained+Deep+Ordinal+Regression+for+End-to-End+Video+Anomaly+Detection+Pang,+Guansong+and+Yan,+Cheng+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton+and+Bai,+Xiao" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Self-trained+Deep+Ordinal+Regression+for+End-to-End+Video+Anomaly+Detection" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1904.10151.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020REVERIE1xxxarXiv.jpg"></a><b>REVERIE: remote embodied visual referring expression in real indoor environments</b>
<br />\(\cdot\) <i>Y. Qi, Q. Wu, P. Anderson, X. Wang, W. Wang, C. Shen, A. van den Hengel</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1904.10151" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020REVERIE1.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={REVERIE}:+Remote+Embodied+Visual+Referring+Expression+in+Real+Indoor+Environments+Qi,+Yuankai+and+Wu,+Qi+and+Anderson,+Peter+and+Wang,+Xin+and+Wang,+William+Yang+and+Shen,+Chunhua+and+{van+den+Hengel},+Anton" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={REVERIE}:+Remote+Embodied+Visual+Referring+Expression+in+Real+Indoor+Environments" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1906.04423.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020NASFCOS10xxxarXiv.jpg"></a><b>NAS-FCOS: fast neural architecture search for object detection</b>
<br />\(\cdot\) <i>N. Wang, Y. Gao, H. Chen, P. Wang, Z. Tian, C. Shen, Y. Zhang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1906.04423" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020NASFCOS10.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={NAS-FCOS}:+Fast+Neural+Architecture+Search+for+Object+Detection+Wang,+Ning+and+Gao,+Yang+and+Chen,+Hao+and+Wang,+Peng+and+Tian,+Zhi+and+Shen,+Chunhua+and+Zhang,+Yanning" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={NAS-FCOS}:+Fast+Neural+Architecture+Search+for+Object+Detection" target=“blank”>semantic scholar</a><a href="https://github.com/Lausannen/NAS-FCOS" target=“blank”>project webpage</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2002.10215.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Wang4xxxarXiv.jpg"></a><b>On the general value of evidence, and bilingual scene-text visual question answering</b>
<br />\(\cdot\) <i>X. Wang, Y. Liu, C. Shen, C. Ng, C. Luo, L. Jin, C. Chan, A. van den Hengel, L. Wang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2002.10215" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Wang4.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=On+the+General+Value+of+Evidence,+and+Bilingual+Scene-Text+Visual+Question+Answering+Wang,+Xinyu+and+Liu,+Yuliang+and+Shen,+Chunhua+and+Ng,+Chun+Chet+and+Luo,+Canjie+and+Jin,+Lianwen+and+Chan,+Chee+Seng+and+{van+den+Hengel},+Anton+and+Wang,+Liangwei" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=On+the+General+Value+of+Evidence,+and+Bilingual+Scene-Text+Visual+Question+Answering" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1909.13226.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Xie8xxxarXiv.jpg"></a><b>PolarMask: single shot instance segmentation with polar representation</b>
<br />\(\cdot\) <i>E. Xie, P. Sun, X. Song, W. Wang, X. Liu, D. Liang, C. Shen, P. Luo</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1909.13226" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Xie8.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={PolarMask}:+Single+Shot+Instance+Segmentation+with+Polar+Representation+Xie,+Enze+and+Sun,+Peize+and+Song,+Xiaoge+and+Wang,+Wenhai+and+Liu,+Xuebo+and+Liang,+Ding+and+Shen,+Chunhua+and+Luo,+Ping" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={PolarMask}:+Single+Shot+Instance+Segmentation+with+Polar+Representation" target=“blank”>semantic scholar</a><a href="https://github.com/xieenze/PolarMask" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2004.01547.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Yu2xxxarXiv.jpg"></a><b>Context prior for scene segmentation</b>
<br />\(\cdot\) <i>C. Yu, J. Wang, C. Gao, G. Yu, C. Shen, N. Sang</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2004.01547" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Yu2.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Context+Prior+for+Scene+Segmentation+Yu,+Changqian+and+Wang,+Jingbo+and+Gao,+Changxin+and+Yu,+Gang+and+Shen,+Chunhua+and+Sang,+Nong" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Context+Prior+for+Scene+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2003.06777.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020EMD9xxxarXiv.jpg"></a><b>DeepEMD: few-shot image classification with differentiable earth mover's distance and structured classifiers</b>
<br />\(\cdot\) <i>C. Zhang, Y. Cai, G. Lin, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2003.06777" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020EMD9.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q={DeepEMD}:+Few-Shot+Image+Classification+with+Differentiable+Earth+Mover's+Distance+and+Structured+Classifiers+Zhang,+Chi+and+Cai,+Yujun+and+Lin,+Guosheng+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q={DeepEMD}:+Few-Shot+Image+Classification+with+Differentiable+Earth+Mover's+Distance+and+Structured+Classifiers" target=“blank”>semantic scholar</a><a href="https://github.com/icoz69/DeepEMD" target=“blank”>project webpage</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1909.08228.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020NAS11xxxarXiv.jpg"></a><b>Memory-efficient hierarchical neural architecture search for image denoising</b>
<br />\(\cdot\) <i>H. Zhang, Y. Li, H. Chen, C. Shen</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1909.08228" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020NAS11.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Memory-Efficient+Hierarchical+Neural+Architecture+Search+for+Image+Denoising+Zhang,+Haokui+and+Li,+Ying+and+Chen,+Hao+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Memory-Efficient+Hierarchical+Neural+Architecture+Search+for+Image+Denoising" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/2003.11712.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Zhang3xxxarXiv.jpg"></a><b>Mask encoding for single shot instance segmentation</b>
<br />\(\cdot\) <i>R. Zhang, Z. Tian, C. Shen, M. You, Y. Yan</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/2003.11712" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Zhang3.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Mask+Encoding+for+Single+Shot+Instance+Segmentation+Zhang,+Rufeng+and+Tian,+Zhi+and+Shen,+Chunhua+and+You,+Mingyu+and+Yan,+Youliang" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Mask+Encoding+for+Single+Shot+Instance+Segmentation" target=“blank”>semantic scholar</a><a href="https://github.com/aim-uofa/AdelaiDet/" target=“blank”>project webpage</a>
</p>
</li>
<li><p><a class="imglink" target="_blank" href="https://arxiv.org/pdf/1903.11236.pdf"><img class="imgP right" src="data/thumbnail/CVPR2020Zhuang5xxxarXiv.jpg"></a><b>Training quantized neural networks with a full-precision auxiliary module</b>
<br />\(\cdot\) <i>B. Zhuang, L. Liu, M. Tan, C. Shen, I. Reid</i>.
<br />\(\cdot\) <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020</i>.
<br />\(\cdot\) <a href="https://arxiv.org/abs/1903.11236" target=“blank”>arXiv</a><a href="data/bibtex/CVPR2020Zhuang5.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Training+Quantized+Neural+Networks+with+a+Full-precision+Auxiliary+Module+Zhuang,+Bohan+and+Liu,+Lingqiao+and+Tan,+Mingkui+and+Shen,+Chunhua+and+Reid,+Ian" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Training+Quantized+Neural+Networks+with+a+Full-precision+Auxiliary+Module" target=“blank”>semantic scholar</a>
</p>
<ol reversed>
<li><p>Oral presentation.
</p>
</li></ol>
</li>
<li><p><b>Learning and memorizing representative prototypes for 3D point cloud semantic and instance segmentation</b>
<br />\(\cdot\) <i>T. He, D. Gong, Z. Tian, C. Shen</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’20), 2020</i>.
<br />\(\cdot\) <a href="data/bibtex/He2020PC1.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Learning+and+Memorizing+Representative+Prototypes+for+{3D}+Point+Cloud+Semantic+and+Instance+Segmentation+He,+Tong+and+Gong,+Dong+and+Tian,+Zhi+and+Shen,+Chunhua" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Learning+and+Memorizing+Representative+Prototypes+for+{3D}+Point+Cloud+Semantic+and+Instance+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>
<li><p><b>Instance-aware embedding for point cloud instance segmentation</b>
<br />\(\cdot\) <i>T. He, Y. Liu, C. Shen, X. Wang, C. Sun</i>.
<br />\(\cdot\) <i>Proc. European Conference on Computer Vision (ECCV’20), 2020</i>.
<br />\(\cdot\) <a href="data/bibtex/He2020InstanceAware.bib" target=“blank”>bibtex</a><a href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Instance-Aware+Embedding+for+Point+Cloud+Instance+Segmentation+He,+Tong+and+Liu,+Yifan+and+Shen,+Chunhua+and+Wang,+Xinlong+and+Sun,+Changming" target=“blank”>google scholar</a><a href="https://www.semanticscholar.org/search?q=Instance-Aware+Embedding+for+Point+Cloud+Instance+Segmentation" target=“blank”>semantic scholar</a>
</p>
</li>