Domain-Class Correlation Decomposition for Generalizable Person Re-Identification

• URL: http://arxiv.org/abs/2106.15206v1
• Date: Tue, 29 Jun 2021 09:45:03 GMT
• Title: Domain-Class Correlation Decomposition for Generalizable Person Re-Identification
• Authors: Kaiwen Yang and Xinmei Tian
• Abstract summary: In person re-identification, the domain and class are correlated. We show that domain adversarial learning will lose certain information about class due to this domain-class correlation. Our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark.
• Score: 34.813965300584776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization in person re-identification is a highly important meaningful and practical task in which a model trained with data from several source domains is expected to generalize well to unseen target domains. Domain adversarial learning is a promising domain generalization method that aims to remove domain information in the latent representation through adversarial training. However, in person re-identification, the domain and class are correlated, and we theoretically show that domain adversarial learning will lose certain information about class due to this domain-class correlation. Inspired by casual inference, we propose to perform interventions to the domain factor $d$, aiming to decompose the domain-class correlation. To achieve this goal, we proposed estimating the resulting representation $z^{*}$ caused by the intervention through first- and second-order statistical characteristic matching. Specifically, we build a memory bank to restore the statistical characteristics of each domain. Then, we use the newly generated samples $\{z^{*},y,d^{*}\}$ to compute the loss function. These samples are domain-class correlation decomposed; thus, we can learn a domain-invariant representation that can capture more class-related features. Extensive experiments show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark.

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