Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption

TL;DR

This paper introduces BNTA, a self-supervised test-time adaptation method for person re-identification that updates batch normalization parameters to handle domain shifts, improving generalization to unseen datasets.

Contribution

The paper proposes BNTA, a novel framework using self-supervised auxiliary tasks to adapt batch normalization during inference for better re-id performance across domains.

Findings

BNTA outperforms existing methods on multiple datasets.

Self-supervised adaptation improves generalization without labeled target data.

The approach effectively captures domain-aware features through auxiliary tasks.

Abstract

In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the state-of-the-art methods.