Generalizable Person Re-identification via Balancing Alignment and Uniformity

• URL: http://arxiv.org/abs/2411.11471v1
• Date: Mon, 18 Nov 2024 11:13:30 GMT
• Title: Generalizable Person Re-identification via Balancing Alignment and Uniformity
• Authors: Yoonki Cho, Jaeyoon Kim, Woo Jae Kim, Junsik Jung, Sung-eui Yoon,
• Abstract summary: Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. Certain augmentations exhibit a polarized effect in this task, enhancing in-distribution performance while deteriorating out-of-distribution performance. We propose a novel framework, Balancing Alignment and Uniformity (BAU), which effectively mitigates this effect by maintaining a balance between alignment and uniformity.
• Score: 22.328800139066914
• License:
• Abstract: Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalization, certain augmentations exhibit a polarized effect in this task, enhancing in-distribution performance while deteriorating out-of-distribution performance. In this paper, we investigate this phenomenon and reveal that it leads to sparse representation spaces with reduced uniformity. To address this issue, we propose a novel framework, Balancing Alignment and Uniformity (BAU), which effectively mitigates this effect by maintaining a balance between alignment and uniformity. Specifically, BAU incorporates alignment and uniformity losses applied to both original and augmented images and integrates a weighting strategy to assess the reliability of augmented samples, further improving the alignment loss. Additionally, we introduce a domain-specific uniformity loss that promotes uniformity within each source domain, thereby enhancing the learning of domain-invariant features. Extensive experimental results demonstrate that BAU effectively exploits the advantages of data augmentation, which previous studies could not fully utilize, and achieves state-of-the-art performance without requiring complex training procedures. The code is available at \url{https://github.com/yoonkicho/BAU}.

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