Clothes-Invariant Feature Learning by Causal Intervention for
  Clothes-Changing Person Re-identification
Xulin Li Yan Lu Bin Liu Yuenan Hou Yating Liu Qi Chu Wanli Ouyang Nenghai Yu
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.