{
  "id": "2305.06145",
  "version": "v1",
  "published": "2023-05-10T13:48:24.000Z",
  "updated": "2023-05-10T13:48:24.000Z",
  "title": "Clothes-Invariant Feature Learning by Causal Intervention for Clothes-Changing Person Re-identification",
  "authors": [
    "Xulin Li",
    "Yan Lu",
    "Bin Liu",
    "Yuenan Hou",
    "Yating Liu",
    "Qi Chu",
    "Wanli Ouyang",
    "Nenghai Yu"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Clothes-invariant feature extraction is critical to the clothes-changing person re-identification (CC-ReID). It can provide discriminative identity features and eliminate the negative effects caused by the confounder--clothing changes. But we argue that there exists a strong spurious correlation between clothes and human identity, that restricts the common likelihood-based ReID method P(Y|X) to extract clothes-irrelevant features. In this paper, we propose a new Causal Clothes-Invariant Learning (CCIL) method to achieve clothes-invariant feature learning by modeling causal intervention P(Y|do(X)). This new causality-based model is inherently invariant to the confounder in the causal view, which can achieve the clothes-invariant features and avoid the barrier faced by the likelihood-based methods. Extensive experiments on three CC-ReID benchmarks, including PRCC, LTCC, and VC-Clothes, demonstrate the effectiveness of our approach, which achieves a new state of the art.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-05-10T13:48:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "clothes-changing person re-identification",
      "clothes-invariant feature learning",
      "clothes-invariant feature extraction",
      "achieve clothes-invariant feature",
      "extract clothes-irrelevant features"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}