Leaning Compact and Representative Features for Cross-Modality Person Re-Identification

Pytorch code for "Leaning Compact and Representative Features for Cross-Modality Person Re-Identification"(World Wide Web,CCF-B).

[Highlights]

1.We devise an efficient Enumerate Angular Triplet (EAT) loss, which can better help to obtain an angularly separable common feature space via explicitly restraining the
internal angles between different embedding features, contributing to the improvement of the performance.
2.Motivated by the knowledge distillation, a novel Cross-Modality Knowledge Distillation (CMKD) loss is proposed to reduce the modality discrepancy in the modality-
specific feature extraction stage, contributing to the effectiveness of the cross-modality person Re-ID task.
3.Our network achieves prominent results on both SYSU-MM01 and RegDB datasets without any other data augment skills. It achieves a Mean Average Precision (mAP) of
43.09% and 79.92% on SYSU-MM01 and RegDB datasets, respectively.

[Prerequisite]

Python>=3.6
Pytorch>=1.0.0
Opencv>=3.1.0
tensorboard-pytorch

[Experiments]

Training:
python main.py -a train
Testing:
python main.py -a test -m checkpoint_name -s test_setting
The test settings of SYSU-MM01 include: "all_multi" (all search mode, multi-shot), "all_single" (all search mode, single-shot), "indoor_multi" (indoor search mode, multi-shot), "indoor_single" (indoor search mode, single-shot).

[Cite]

If you find our paper/codes useful, please kindly consider citing the paper:
@article{gao2022leaning,
title={Leaning compact and representative features for cross-modality person re-identification},
author={Gao, Guangwei and Shao, Hao and Wu, Fei and Yang, Meng and Yu, Yi},
journal={World Wide Web},
pages={1--18},
year={2022},
publisher={Springer}
}