Unified Batch All Triplet Loss for Visible-Infrared Person Re-identification

TL;DR

This paper proposes a novel unified batch all triplet loss method combined with cosine softmax loss for improved visible-infrared person re-identification, addressing modality imbalance issues and outperforming existing methods.

Contribution

It introduces a unified batch all triplet loss and reformulates existing losses to enhance cross-modality person re-identification performance.

Findings

Outperforms state-of-the-art methods significantly.

Effectively addresses modality imbalance in VI-ReID.

Demonstrates the effectiveness of batch all triplet strategy.

Abstract

Visible-Infrared cross-modality person re-identification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Batch Hard Triplet loss is widely used in person re-identification tasks, but it does not perform well in the Visible-Infrared person re-identification task. Because it only optimizes the hardest triplet for each anchor image within the mini-batch, samples in the hardest triplet may all belong to the same modality, which will lead to the imbalance problem of modality optimization. To address this problem, we adopt the batch all triplet selection strategy, which selects all the possible triplets among samples to optimize instead of the hardest triplet. Furthermore, we introduce Unified Batch All Triplet loss and Cosine Softmax loss to collaboratively optimize the cosine distance between image vectors. Similarly, we rewrite the Hetero Center Triplet loss, which is proposed for VI-ReID task, into a batch all form to improve model performance. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.