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:
this https URL

该研究提出了一种基于衣服的对抗损失 (CAL) 方法，从原始 RGB 图像中挖掘与衣服无关的特征来提高衣服变化人员重新识别的准确度。同时，也贡献了一个名为 CCVID 的数据集，为对建模时空信息提供实验基础。