Generalizable Person Re-identification via Balancing Alignment and Uniformity

TL;DR

This paper introduces BAU, a framework that balances alignment and uniformity in feature representations to improve domain generalization in person re-identification, effectively leveraging data augmentation.

Contribution

It proposes a novel balancing framework that maintains alignment and uniformity, including a domain-specific uniformity loss, to enhance domain-invariant feature learning.

Findings

Achieves state-of-the-art performance on DG re-ID benchmarks.

Effectively mitigates the negative polarization effect of data augmentation.

Demonstrates robustness without complex training procedures.

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}.