Generalizable Metric Network for Cross-domain Person Re-identification

• URL: http://arxiv.org/abs/2306.11991v2
• Date: Mon, 29 Apr 2024 06:45:43 GMT
• Title: Generalizable Metric Network for Cross-domain Person Re-identification
• Authors: Lei Qi, Ziang Liu, Yinghuan Shi, Xin Geng,
• Abstract summary: Cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks. Most existing methods aim to learn domain-invariant or robust features for all domains. We propose a Generalizable Metric Network (GMN) to explore sample similarity in the sample-pair space.
• Score: 55.71632958027289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackle this challenge, most existing methods aim to learn domain-invariant or robust features for all domains. In this paper, we observe that the data-distribution gap between the training and test sets is smaller in the sample-pair space than in the sample-instance space. Based on this observation, we propose a Generalizable Metric Network (GMN) to further explore sample similarity in the sample-pair space. Specifically, we add a Metric Network (M-Net) after the main network and train it on positive and negative sample-pair features, which is then employed during the test stage. Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity. Moreover, we develop a Pair-Identity Center (PIC) loss to enhance the model's discrimination by ensuring that sample-pair features with the same pair-identity are consistent. We validate the effectiveness of our proposed method through a lot of experiments on multiple benchmark datasets and confirm the value of each module in our GMN.

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