Clothes-Invariant Feature Learning by Causal Intervention for Clothes-Changing Person Re-identification

• URL: http://arxiv.org/abs/2305.06145v1
• Date: Wed, 10 May 2023 13:48:24 GMT
• 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
• Abstract summary: Clothes-invariant feature extraction is critical to the clothes-changing person re-identification (CC-ReID) 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. We propose a new Causal Clothes-Invariant Learning (CCIL) method to achieve clothes-invariant feature learning.
• Score: 118.23912884472794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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