{
  "id": "2204.06890",
  "version": "v1",
  "published": "2022-04-14T11:38:28.000Z",
  "updated": "2022-04-14T11:38:28.000Z",
  "title": "Clothes-Changing Person Re-identification with RGB Modality Only",
  "authors": [
    "Xinqian Gu",
    "Hong Chang",
    "Bingpeng Ma",
    "Shutao Bai",
    "Shiguang Shan",
    "Xilin Chen"
  ],
  "comment": "Accepted by CVPR 2022",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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: https://github.com/guxinqian/Simple-CCReID.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-04-14T11:38:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "clothes-changing person re-identification",
      "rgb modality",
      "clothes-changing person re-id benchmarks",
      "original rgb images",
      "model proper spatiotemporal patterns"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}