Clothes-Changing Person Re-identification with RGB Modality Only

The key to address clothes-changing person re-identification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multi-modality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information. The code and new dataset are available at:

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Multiview Gait Recognition CASIA-B CAL (RGB), AP3DNLResNet50 Accuracy (Cross-View, Avg) 97.3 # 1
NM#5-6 99.9 # 1
BG#1-2 99.8 # 1
CL#1-2 92.3 # 1
Person Re-Identification LTCC CAL Rank-1 40.1 # 6
mAP 18.0 # 6
Person Re-Identification PRCC CAL Rank-1 55.2 # 2
mAP 55.8 # 4


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