Label Distribution Learning for Generalizable Multi-source Person Re-identification

TL;DR

This paper introduces a label distribution learning approach for multi-source person re-identification that enhances domain generalization by modeling class relations and reducing domain gaps, leading to improved performance on unseen domains.

Contribution

The paper proposes a novel LDL method that mines class relations and balances attention across domains to learn domain-invariant features for generalizable person Re-ID.

Findings

Outperforms state-of-the-art methods on multiple benchmarks.

Effectively reduces domain gap and improves generalization.

Theoretically demonstrates robustness to domain shift.

Abstract

Person re-identification (Re-ID) is a critical technique in the video surveillance system, which has achieved significant success in the supervised setting. However, it is difficult to directly apply the supervised model to arbitrary unseen domains due to the domain gap between the available source domains and unseen target domains. In this paper, we propose a novel label distribution learning (LDL) method to address the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training), which aims to explore the relation of different classes and mitigate the domain-shift across different domains so as to improve the discrimination of the model and learn the domain-invariant feature, simultaneously. Specifically, during the training process, we produce the label distribution via the online manner to mine the relation information of different classes, thus it is beneficial for extracting the discriminative feature. Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature. Furthermore, we also give the theoretical analysis to demonstrate that the proposed method can effectively deal with the domain-shift issue. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.