本文来源于毕业于韩国首尔国立大学电气与计算机工程专业的 Lee hoseong。项目地址是： https://github.com/hoya012/deep_learning_object_detection
目标检测作为计算机视觉中的一个重要分支，近些年来随着神经网络理论研究的深入和硬件 GPU 算力的大幅度提升，一举成为全球人工智能研究的热点，落地项目也最先开始。
纵观 2013 年到 2020 年，从最早的 R-CNN、OverFeat 到后来的 SSD、YOLO v3 再到去年的 M2Det，新模型层出不穷，性能也越来越好！本文将完整地总结 52 个目标检测模型极其性能对比，包括完备的文献 paper 列表。
该技术路线纵贯的时间线是 2013 年到 2020 年初，上图总结了这期间目标检测所有具有代表性的模型。图中标红的部分是相对来说比较重要，需要重点掌握的模型。
由于硬件不同（例如 CPU、GPU、RAM 等），来比较 FPS 往往不够准确。更合适的比较方法是在同一硬件配置下测量所有模型的性能。以上所有模型的性能对比结果如下：
| Detector | VOC07 (mAP@IoU=0.5) | VOC12 (mAP@IoU=0.5) | COCO (mAP@IoU=0.5:0.95) | Published In |
|---|---|---|---|---|
| R-CNN | 58.5 | - | - | CVPR'14 |
| SPP-Net | 59.2 | - | - | ECCV'14 |
| MR-CNN | 78.2 (07+12) | 73.9 (07+12) | - | ICCV'15 |
| Fast R-CNN | 70.0 (07+12) | 68.4 (07++12) | 19.7 | ICCV'15 |
| Faster R-CNN | 73.2 (07+12) | 70.4 (07++12) | 21.9 | NIPS'15 |
| YOLO v1 | 66.4 (07+12) | 57.9 (07++12) | - | CVPR'16 |
| G-CNN | 66.8 | 66.4 (07+12) | - | CVPR'16 |
| AZNet | 70.4 | - | 22.3 | CVPR'16 |
| ION | 80.1 | 77.9 | 33.1 | CVPR'16 |
| HyperNet | 76.3 (07+12) | 71.4 (07++12) | - | CVPR'16 |
| OHEM | 78.9 (07+12) | 76.3 (07++12) | 22.4 | CVPR'16 |
| MPN | - | - | 33.2 | BMVC'16 |
| SSD | 76.8 (07+12) | 74.9 (07++12) | 31.2 | ECCV'16 |
| GBDNet | 77.2 (07+12) | - | 27.0 | ECCV'16 |
| CPF | 76.4 (07+12) | 72.6 (07++12) | - | ECCV'16 |
| R-FCN | 79.5 (07+12) | 77.6 (07++12) | 29.9 | NIPS'16 |
| DeepID-Net | 69.0 | - | - | PAMI'16 |
| NoC | 71.6 (07+12) | 68.8 (07+12) | 27.2 | TPAMI'16 |
| DSSD | 81.5 (07+12) | 80.0 (07++12) | 33.2 | arXiv'17 |
| TDM | - | - | 37.3 | CVPR'17 |
| FPN | - | - | 36.2 | CVPR'17 |
| YOLO v2 | 78.6 (07+12) | 73.4 (07++12) | - | CVPR'17 |
| RON | 77.6 (07+12) | 75.4 (07++12) | 27.4 | CVPR'17 |
| DeNet | 77.1 (07+12) | 73.9 (07++12) | 33.8 | ICCV'17 |
| CoupleNet | 82.7 (07+12) | 80.4 (07++12) | 34.4 | ICCV'17 |
| RetinaNet | - | - | 39.1 | ICCV'17 |
| DSOD | 77.7 (07+12) | 76.3 (07++12) | - | ICCV'17 |
| SMN | 70.0 | - | - | ICCV'17 |
| Light-Head R-CNN | - | - | 41.5 | arXiv'17 |
| YOLO v3 | - | - | 33.0 | arXiv'18 |
| SIN | 76.0 (07+12) | 73.1 (07++12) | 23.2 | CVPR'18 |
| STDN | 80.9 (07+12) | - | - | CVPR'18 |
| RefineDet | 83.8 (07+12) | 83.5 (07++12) | 41.8 | CVPR'18 |
| SNIP | - | - | 45.7 | CVPR'18 |
| Relation-Network | - | - | 32.5 | CVPR'18 |
| Cascade R-CNN | - | - | 42.8 | CVPR'18 |
| MLKP | 80.6 (07+12) | 77.2 (07++12) | 28.6 | CVPR'18 |
| Fitness-NMS | - | - | 41.8 | CVPR'18 |
| RFBNet | 82.2 (07+12) | - | - | ECCV'18 |
| CornerNet | - | - | 42.1 | ECCV'18 |
| PFPNet | 84.1 (07+12) | 83.7 (07++12) | 39.4 | ECCV'18 |
| Pelee | 70.9 (07+12) | - | - | NIPS'18 |
| HKRM | 78.8 (07+12) | - | 37.8 | NIPS'18 |
| M2Det | - | - | 44.2 | AAAI'19 |
| R-DAD | 81.2 (07++12) | 82.0 (07++12) | 43.1 | AAAI'19 |
| ScratchDet | 84.1 (07++12) | 83.6 (07++12) | 39.1 | CVPR'19 |
| Libra R-CNN | - | - | 43.0 | CVPR'19 |
| Reasoning-RCNN | 82.5 (07++12) | - | 43.2 | CVPR'19 |
| FSAF | - | - | 44.6 | CVPR'19 |
| AmoebaNet + NAS-FPN | - | - | 47.0 | CVPR'19 |
| Cascade-RetinaNet | - | - | 41.1 | CVPR'19 |
| HTC | - | - | 47.2 | CVPR'19 |
| TridentNet | - | - | 48.4 | ICCV'19 |
| DAFS | 85.3 (07+12) | 83.1 (07++12) | 40.5 | ICCV'19 |
| Auto-FPN | 81.8 (07++12) | - | 40.5 | ICCV'19 |
| FCOS | - | - | 44.7 | ICCV'19 |
| FreeAnchor | - | - | 44.8 | NeurIPS'19 |
| DetNAS | 81.5 (07++12) | - | 42.0 | NeurIPS'19 |
| NATS | - | - | 42.0 | NeurIPS'19 |
| AmoebaNet + NAS-FPN + AA | - | - | 50.7 | arXiv'19 |
| SpineNet | - | - | 52.1 | arXiv'19 |
| CBNet | - | - | 53.3 | AAAI'20 |
| EfficientDet | - | - | 52.6 | CVPR'20 |
| DetectoRS | - | - | 54.7 | arXiv'20 |
从上面的表格中，可以清楚看到不同模型在 VOC07、VOC12、COCO 数据集上的性能表现。同时列出了模型论文发表来源。
下面列举一些重点标红的模型进行简要介绍。
-
[R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR' 14] |
[pdf][official code - caffe] -
[OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR' 14] |
[pdf][official code - torch] -
[MultiBox] Scalable Object Detection using Deep Neural Networks | [CVPR' 14] |
[pdf] -
[SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | [ECCV' 14] |
[pdf][official code - caffe][unofficial code - keras][unofficial code - tensorflow]
-
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [CVPR' 15] |
[pdf][official code - matlab] -
[MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | [ICCV' 15] |
[pdf][official code - caffe] -
[DeepBox] DeepBox: Learning Objectness with Convolutional Networks | [ICCV' 15] |
[pdf][official code - caffe] -
[AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | [ICCV' 15] |
[pdf] -
[Fast R-CNN] Fast R-CNN | [ICCV' 15] |
[pdf][official code - caffe] -
[DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | [ICCV' 15] |
[pdf][official code - matconvnet] -
[Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS' 15] |
[pdf][official code - caffe][unofficial code - tensorflow][unofficial code - pytorch]
-
[YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR' 16] |
[pdf][official code - c] -
[G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR' 16] |
[pdf] -
[AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR' 16] |
[pdf] -
[ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR' 16] |
[pdf] -
[HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR' 16] |
[pdf] -
[OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR' 16] |
[pdf][official code - caffe] -
[CRAPF] CRAFT Objects from Images | [CVPR' 16] |
[pdf][official code - caffe] -
[MPN] A MultiPath Network for Object Detection | [BMVC' 16] |
[pdf][official code - torch] -
[SSD] SSD: Single Shot MultiBox Detector | [ECCV' 16] |
[pdf][official code - caffe][unofficial code - tensorflow][unofficial code - pytorch] -
[GBDNet] Crafting GBD-Net for Object Detection | [ECCV' 16] |
[pdf][official code - caffe] -
[CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV' 16] |
[pdf] -
[MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV' 16] |
[pdf][official code - caffe] -
[R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS' 16] |
[pdf][official code - caffe][unofficial code - caffe] -
[PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW' 16] |
[pdf][official code - caffe] -
[DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI' 16] |
[pdf] -
[NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI' 16] |
[pdf]
-
[DSSD] DSSD : Deconvolutional Single Shot Detector | [arXiv' 17] |
[pdf][official code - caffe] -
[TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | [CVPR' 17] |
[pdf] -
[FPN] Feature Pyramid Networks for Object Detection | [CVPR' 17] |
[pdf][unofficial code - caffe] -
[YOLO v2] YOLO9000: Better, Faster, Stronger | [CVPR' 17] |
[pdf][official code - c][unofficial code - caffe][unofficial code - tensorflow][unofficial code - tensorflow][unofficial code - pytorch] -
[RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | [CVPR' 17] |
[pdf][official code - caffe][unofficial code - tensorflow] -
[RSA] Recurrent Scale Approximation for Object Detection in CNN | | [ICCV' 17] |
[pdf][official code - caffe] -
[DCN] Deformable Convolutional Networks | [ICCV' 17] |
[pdf][official code - mxnet][unofficial code - tensorflow][unofficial code - pytorch] -
[DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | [ICCV' 17] |
[pdf][official code - theano] -
[CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | [ICCV' 17] |
[pdf][official code - caffe] -
[RetinaNet] Focal Loss for Dense Object Detection | [ICCV' 17] |
[pdf][official code - keras][unofficial code - pytorch][unofficial code - mxnet][unofficial code - tensorflow] -
[Mask R-CNN] Mask R-CNN | [ICCV' 17] |
[pdf][official code - caffe2][unofficial code - tensorflow][unofficial code - tensorflow][unofficial code - pytorch] -
[DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | [ICCV' 17] |
[pdf][official code - caffe][unofficial code - pytorch] -
[SMN] Spatial Memory for Context Reasoning in Object Detection | [ICCV' 17] |
[pdf] -
[Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | [arXiv' 17] |
[pdf][official code - tensorflow] -
[Soft-NMS] Improving Object Detection With One Line of Code | [ICCV' 17] |
[pdf][official code - caffe]
-
[YOLO v3] YOLOv3: An Incremental Improvement | [arXiv' 18] |
[pdf][official code - c][unofficial code - pytorch][unofficial code - pytorch][unofficial code - keras][unofficial code - tensorflow] -
[ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV' 18] |
[pdf][official code - caffe] -
[SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR' 18] |
[pdf][official code - tensorflow] -
[STDN] Scale-Transferrable Object Detection | [CVPR' 18] |
[pdf] -
[RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR' 18] |
[pdf][official code - caffe][unofficial code - chainer][unofficial code - pytorch] -
[MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR' 18] |
[pdf] -
[DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR' 18] |
[pdf][official code - caffe] -
[SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR' 18] |
[pdf] -
[Relation-Network] Relation Networks for Object Detection | [CVPR' 18] |
[pdf][official code - mxnet] -
[Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR' 18] |
[pdf][official code - caffe] -
Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR' 18] |
[pdf] -
[MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR' 18] |
[pdf][official code - caffe] -
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR' 18] |
[pdf][official code - chainer] -
[Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR' 18] |
[pdf] -
[STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC' 18] |
[pdf] -
[RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV' 18] |
[pdf][official code - pytorch] -
Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV' 18] |
[pdf] -
[CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV' 18] |
[pdf][official code - pytorch] -
[PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV' 18] |
[pdf] -
[Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv' 18] |
[pdf] -
[ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD' 18] |
[pdf][official code - tensorflow] -
[Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS' 18] |
[pdf][official code - caffe] -
[HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS' 18] |
[pdf] -
[MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS' 18] |
[pdf] -
[SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS' 18] |
[pdf]
-
[M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI' 19] |
[pdf][official code - pytorch] -
[R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI' 19] |
[pdf] -
[CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR' 19] |
[pdf] -
Feature Intertwiner for Object Detection | [ICLR' 19] |
[pdf] -
[GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR' 19] |
[pdf] -
Automatic adaptation of object detectors to new domains using self-training | [CVPR' 19] |
[pdf] -
[Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR' 19] |
[pdf] -
[FSAF] Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR' 19] |
[pdf] -
[ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR' 19] |
[pdf]|[official code - pytorch] -
[C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection | [CVPR' 19] |
[pdf]|[official code - torch] -
[ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR' 19] |
[pdf] -
Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR' 19] |
[pdf]|[official code - caffe2] -
Activity Driven Weakly Supervised Object Detection | [CVPR' 19] |
[pdf] -
Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR' 19] |
[pdf] -
Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR' 19] |
[pdf]|[official code - pytorch] -
[NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR' 19] |
[pdf] -
[Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR' 19] |
[pdf] -
Point in, Box out: Beyond Counting Persons in Crowds | [CVPR' 19] |
[pdf] -
Locating Objects Without Bounding Boxes | [CVPR' 19] |
[pdf] -
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR' 19] |
[pdf] -
Towards Universal Object Detection by Domain Attention | [CVPR' 19] |
[pdf] -
Exploring the Bounds of the Utility of Context for Object Detection | [CVPR' 19] |
[pdf] -
What Object Should I Use? - Task Driven Object Detection | [CVPR' 19] |
[pdf] -
Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR' 19] |
[pdf] -
Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR' 19] |
[pdf] -
Fully Quantized Network for Object Detection | [CVPR' 19] |
[pdf] -
Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR' 19] |
[pdf] -
Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR' 19] |
[pdf] -
[Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR' 19] |
[pdf] -
Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR' 19] |
[pdf] -
Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR' 19] |
[pdf] -
Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR' 19] |
[pdf] -
[MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR' 19] |
[pdf] -
You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR' 19] |
[pdf] -
Object detection with location-aware deformable convolution and backward attention filtering | [CVPR' 19] |
[pdf] -
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR' 19] |
[pdf] -
Hybrid Task Cascade for Instance Segmentation | [CVPR' 19] |
[pdf] -
[GFR] Improving Object Detection from Scratch via Gated Feature Reuse | [BMVC' 19] |
[pdf]|[official code - pytorch] -
[Cascade RetinaNet] Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection | [BMVC' 19] |
[pdf] -
Soft Sampling for Robust Object Detection | [BMVC' 19] |
[pdf] -
Multi-adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV' 19] |
[pdf] -
Towards Adversarially Robust Object Detection | [ICCV' 19] |
[pdf] -
A Robust Learning Approach to Domain Adaptive Object Detection | [ICCV' 19] |
[pdf] -
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell | [ICCV' 19] |
[pdf] -
Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach | [ICCV' 19] |
[pdf] -
Employing Deep Part-Object Relationships for Salient Object Detection | [ICCV' 19] |
[pdf] -
Learning Rich Features at High-Speed for Single-Shot Object Detection | [ICCV' 19] |
[pdf] -
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection | [ICCV' 19] |
[pdf] -
Selectivity or Invariance: Boundary-Aware Salient Object Detection | [ICCV' 19] |
[pdf] -
Progressive Sparse Local Attention for Video Object Detection | [ICCV' 19] |
[pdf] -
Minimum Delay Object Detection From Video | [ICCV' 19] |
[pdf] -
Towards Interpretable Object Detection by Unfolding Latent Structures | [ICCV' 19] |
[pdf] -
Scaling Object Detection by Transferring Classification Weights | [ICCV' 19] |
[pdf] -
[TridentNet] Scale-Aware Trident Networks for Object Detection | [ICCV' 19] |
[pdf] -
Generative Modeling for Small-Data Object Detection | [ICCV' 19] |
[pdf] -
Transductive Learning for Zero-Shot Object Detection | [ICCV' 19] |
[pdf] -
Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection | [ICCV' 19] |
[pdf] -
[CenterNet] CenterNet: Keypoint Triplets for Object Detection | [ICCV' 19] |
[pdf] -
[DAFS] Dynamic Anchor Feature Selection for Single-Shot Object Detection | [ICCV' 19] |
[pdf] -
[Auto-FPN] Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification | [ICCV' 19] |
[pdf] -
Multi-Adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV' 19] |
[pdf] -
Object Guided External Memory Network for Video Object Detection | [ICCV' 19] |
[pdf] -
[ThunderNet] ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices | [ICCV' 19] |
[pdf] -
[RDN] Relation Distillation Networks for Video Object Detection | [ICCV' 19] |
[pdf] -
[MMNet] Fast Object Detection in Compressed Video | [ICCV' 19] |
[pdf] -
Towards High-Resolution Salient Object Detection | [ICCV' 19] |
[pdf] -
[SCAN] Stacked Cross Refinement Network for Edge-Aware Salient Object Detection | [ICCV' 19] |
[official code]|[pdf] -
Motion Guided Attention for Video Salient Object Detection | [ICCV' 19] |
[pdf] -
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | [ICCV' 19] |
[pdf] -
Learning to Rank Proposals for Object Detection | [ICCV' 19] |
[pdf] -
[WSOD2] WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection | [ICCV' 19] |
[pdf] -
[ClusDet] Clustered Object Detection in Aerial Images | [ICCV' 19] |
[pdf] -
Towards Precise End-to-End Weakly Supervised Object Detection Network | [ICCV' 19] |
[pdf] -
Few-Shot Object Detection via Feature Reweighting | [ICCV' 19] |
[pdf] -
[Objects365] Objects365: A Large-Scale, High-Quality Dataset for Object Detection | [ICCV' 19] |
[pdf] -
[EGNet] EGNet: Edge Guidance Network for Salient Object Detection | [ICCV' 19] |
[pdf] -
Optimizing the F-Measure for Threshold-Free Salient Object Detection | [ICCV' 19] |
[pdf] -
Sequence Level Semantics Aggregation for Video Object Detection | [ICCV' 19] |
[pdf] -
[NOTE-RCNN] NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection | [ICCV' 19] |
[pdf] -
Enriched Feature Guided Refinement Network for Object Detection | [ICCV' 19] |
[pdf] -
[POD] POD: Practical Object Detection With Scale-Sensitive Network | [ICCV' 19] |
[pdf] -
[FCOS] FCOS: Fully Convolutional One-Stage Object Detection | [ICCV' 19] |
[pdf] -
[RepPoints] RepPoints: Point Set Representation for Object Detection | [ICCV' 19] |
[pdf] -
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection | [ICCV' 19] |
[pdf] -
Weakly Supervised Object Detection With Segmentation Collaboration | [ICCV' 19] |
[pdf] -
Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection | [ICCV' 19] |
[pdf] -
Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes | [ICCV' 19] |
[pdf] -
[C-MIDN] C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection | [ICCV' 19] |
[pdf] -
Meta-Learning to Detect Rare Objects | [ICCV' 19] |
[pdf] -
[Cap2Det] Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection | [ICCV' 19] |
[pdf] -
[Gaussian YOLOv3] Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | [ICCV' 19] |
[pdf][official code - c] -
[FreeAnchor] FreeAnchor: Learning to Match Anchors for Visual Object Detection | [NeurIPS' 19] |
[pdf] -
Memory-oriented Decoder for Light Field Salient Object Detection | [NeurIPS' 19] |
[pdf] -
One-Shot Object Detection with Co-Attention and Co-Excitation | [NeurIPS' 19] |
[pdf] -
[DetNAS] DetNAS: Backbone Search for Object Detection | [NeurIPS' 19] |
[pdf] -
Consistency-based Semi-supervised Learning for Object detection | [NeurIPS' 19] |
[pdf] -
[NATS] Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection | [NeurIPS' 19] |
[pdf] -
[AA] Learning Data Augmentation Strategies for Object Detection | [arXiv' 19] |
[pdf] -
[Spinenet] Spinenet: Learning scale-permuted backbone for recognition and localization | [arXiv' 19] |
[pdf] -
Object Detection in 20 Years: A Survey | [arXiv' 19] |
[pdf]
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[Spiking-YOLO] Spiking-YOLO: Spiking Neural Network for Real-time Object Detection | [AAAI' 20] |
[pdf] -
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction | [AAAI' 20] |
[pdf] -
[CBnet] Cbnet: A novel composite backbone network architecture for object detection | [AAAI' 20] |
[pdf] -
[Distance-IoU Loss] Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | [AAAI' 20] |
[pdf] -
Computation Reallocation for Object Detection | [ICLR' 20] |
[pdf] -
[YOLOv4] YOLOv4: Optimal Speed and Accuracy of Object Detection | [arXiv' 20] |
[pdf] -
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector | [CVPR' 20] |
[pdf] -
Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels | [CVPR' 20] |
[pdf] -
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | [CVPR' 20] |
[pdf] -
Rethinking Classification and Localization for Object Detection | [CVPR' 20] |
[pdf] -
Multiple Anchor Learning for Visual Object Detection | [CVPR' 20] |
[pdf] -
[CentripetalNet] CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection | [CVPR' 20] |
[pdf] -
Learning From Noisy Anchors for One-Stage Object Detection | [CVPR' 20] |
[pdf] -
[EfficientDet] EfficientDet: Scalable and Efficient Object Detection | [CVPR' 20] |
[pdf] -
Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax | [CVPR' 20] |
[pdf] -
Dynamic Refinement Network for Oriented and Densely Packed Object Detection | [CVPR' 20] |
[pdf] -
Noise-Aware Fully Webly Supervised Object Detection | [CVPR' 20] |
[pdf] -
[Hit-Detector] Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | [CVPR' 20] |
[pdf] -
[D2Det] D2Det: Towards High Quality Object Detection and Instance Segmentation | [CVPR' 20] |
[pdf] -
Prime Sample Attention in Object Detection | [CVPR' 20] |
[pdf] -
Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection | [CVPR' 20] |
[pdf] -
Exploring Categorical Regularization for Domain Adaptive Object Detection | [CVPR' 20] |
[pdf] -
[SP-NAS] SP-NAS: Serial-to-Parallel Backbone Search for Object Detection | [CVPR' 20] |
[pdf] -
[NAS-FCOS] NAS-FCOS: Fast Neural Architecture Search for Object Detection | [CVPR' 20] |
[pdf] -
[DR Loss] DR Loss: Improving Object Detection by Distributional Ranking | [CVPR' 20] |
[pdf] -
Detection in Crowded Scenes: One Proposal, Multiple Predictions | [CVPR' 20] |
[pdf] -
[AugFPN] AugFPN: Improving Multi-Scale Feature Learning for Object Detection | [CVPR' 20] |
[pdf] -
Robust Object Detection Under Occlusion With Context-Aware CompositionalNets | [CVPR' 20] |
[pdf] -
Cross-Domain Document Object Detection: Benchmark Suite and Method | [CVPR' 20] |
[pdf] -
Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection | [CVPR' 20] |
[pdf] -
[SLV] SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection | [CVPR' 20] |
[pdf] -
[HAMBox] HAMBox: Delving Into Mining High-Quality Anchors on Face Detection | [CVPR' 20] |
[pdf] -
[Context R-CNN] Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection | [CVPR' 20] |
[pdf] -
Mixture Dense Regression for Object Detection and Human Pose Estimation | [CVPR' 20] |
[pdf] -
Offset Bin Classification Network for Accurate Object Detection | [CVPR' 20] |
[pdf] -
[NETNet] NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection | [CVPR' 20] |
[pdf] -
Scale-Equalizing Pyramid Convolution for Object Detection | [CVPR' 20] |
[pdf] -
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians | [CVPR' 20] |
[pdf] -
[MnasFPN] MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices | [CVPR' 20] |
[pdf] -
Physically Realizable Adversarial Examples for LiDAR Object Detection | [CVPR' 20] |
[pdf] -
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation | [CVPR' 20] |
[pdf] -
Incremental Few-Shot Object Detection | [CVPR' 20] |
[pdf] -
Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection | [CVPR' 20] |
[pdf] -
Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation | [CVPR' 20] |
[pdf] -
Learning a Unified Sample Weighting Network for Object Detection | [CVPR' 20] |
[pdf] -
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization | [CVPR' 20] |
[pdf] -
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution | [arXiv' 20] |
[pdf] -
[DETR] End-to-End Object Detection with Transformers | [ECCV' 20] |
[pdf] -
Suppress and Balance: A Simple Gated Network for Salient Object Detection | [ECCV' 20] |
[code] -
[BorderDet] BorderDet: Border Feature for Dense Object Detection | [ECCV' 20] |
[pdf] -
Corner Proposal Network for Anchor-free, Two-stage Object Detection | [ECCV' 20] |
[pdf] -
A General Toolbox for Understanding Errors in Object Detection | [ECCV' 20] |
[pdf] -
[Chained-Tracker] Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking | [ECCV' 20] |
[pdf] -
Side-Aware Boundary Localization for More Precise Object Detection | [ECCV' 20] |
[pdf] -
[PIoU] PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments | [ECCV' 20] |
[pdf] -
[AABO] AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling | [ECCV' 20] |
[pdf] -
Highly Efficient Salient Object Detection with 100K Parameters | [ECCV' 20] |
[pdf] -
[GeoGraph] GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end | [ECCV' 20] |
[pdf] -
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection| [ECCV' 20] |
[pdf] -
Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection | [ECCV' 20] |
[pdf] -
Arbitrary-Oriented Object Detection with Circular Smooth Label | [ECCV' 20] |
[pdf] -
Soft Anchor-Point Object Detection | [ECCV' 20] |
[pdf] -
Object Detection with a Unified Label Space from Multiple Datasets | [ECCV' 20] |
[pdf] -
[MimicDet] MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection | [ECCV' 20] |
[pdf] -
Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions | [ECCV' 20] |
[pdf] -
[Dynamic R-CNN] Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | [ECCV' 20] |
[pdf] -
[OS2D] OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features | [ECCV' 20] |
[pdf] -
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection | [ECCV' 20] |
[pdf] -
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild | [ECCV' 20] |
[pdf] -
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection | [ECCV' 20] |
[pdf] -
Two-Stream Active Query Suggestion for Large-Scale Object Detection in Connectomics | [ECCV' 20] |
[pdf] -
[FDTS] FDTS: Fast Diverse-Transformation Search for Object Detection and Beyond | [ECCV' 20]
-
Dual refinement underwater object detection network | [ECCV' 20] |
[pdf] -
[APRICOT] APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection | [ECCV' 20] |
[pdf] -
Large Batch Optimization for Object Detection: Training COCO in 12 Minutes | [ECCV' 20] |
[pdf] -
Hierarchical Context Embedding for Region-based Object Detection | [ECCV' 20] |
[pdf] -
Pillar-based Object Detection for Autonomous Driving | [ECCV' 20] |
[pdf] -
Dive Deeper Into Box for Object Detection | [ECCV' 20] |
[pdf] -
Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN | [ECCV' 20] |
[pdf] -
Probabilistic Anchor Assignment with IoU Prediction for Object Detection | [ECCV' 20] |
[pdf] -
[HoughNet] HoughNet: Integrating near and long-range evidence for bottom-up object detection | [ECCV' 20] |
[pdf] -
[LabelEnc] LabelEnc: A New Intermediate Supervision Method for Object Detection | [ECCV' 20] |
[pdf] -
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer | [ECCV' 20] |
[pdf] -
On the Importance of Data Augmentation for Object Detection | [ECCV' 20] |
[pdf] -
Adaptive Object Detection with Dual Multi-Label Prediction | [ECCV' 20] |
[pdf] -
Quantum-soft QUBO Suppression for Accurate Object Detection | [ECCV' 20] |
[pdf] -
Improving Object Detection with Selective Self-supervised Self-training | [ECCV' 20] |
[pdf]
同时也列出了以上模型通常使用的公开数据集：VOC、ILSVRC、COCO，如下表所示：
用于目标检测的数据集相关论文如下：
-
[PASCAL VOC] The PASCAL Visual Object Classes (VOC) Challenge | [IJCV' 10] |
[pdf] -
[PASCAL VOC] The PASCAL Visual Object Classes Challenge: A Retrospective | [IJCV' 15] |
[pdf]|[link] -
[ImageNet] ImageNet: A Large-Scale Hierarchical Image Database| [CVPR' 09] |
[pdf] -
[ImageNet] ImageNet Large Scale Visual Recognition Challenge | [IJCV' 15] |
[pdf]|[link] -
[COCO] Microsoft COCO: Common Objects in Context | [ECCV' 14] |
[pdf]|[link] -
[Open Images] The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale | [arXiv' 18] |
[pdf]|[link] -
[DOTA] DOTA: A Large-scale Dataset for Object Detection in Aerial Images | [CVPR' 18] |
[pdf]|[link] -
[Objects365] Objects365: A Large-Scale, High-Quality Dataset for Object Detection | [ICCV' 19] |
[link]