Mining False Positive Examples for Text-Based Person Re-identification

TL;DR

This paper introduces a novel multi-branch architecture called MFPE for text-based person re-identification, focusing on mining false positive examples to better handle mismatched word-region pairs and improve matching accuracy.

Contribution

The paper proposes a new false positive mining approach with a specialized loss function to address inter-modal gaps in text-based person ReID, enhancing matching performance.

Findings

MFPE outperforms existing methods on CUHK-PEDES dataset.

The false positive mining strategy improves discrimination between matched and mismatched pairs.

The cross-relu loss effectively increases the similarity gap.

Abstract

Text-based person re-identification (ReID) aims to identify images of the targeted person from a large-scale person image database according to a given textual description. However, due to significant inter-modal gaps, text-based person ReID remains a challenging problem. Most existing methods generally rely heavily on the similarity contributed by matched word-region pairs, while neglecting mismatched word-region pairs which may play a decisive role. Accordingly, we propose to mine false positive examples (MFPE) via a jointly optimized multi-branch architecture to handle this problem. MFPE contains three branches including a false positive mining (FPM) branch to highlight the role of mismatched word-region pairs. Besides, MFPE delicately designs a cross-relu loss to increase the gap of similarity scores between matched and mismatched word-region pairs. Extensive experiments on CUHK-PEDES demonstrate the superior effectiveness of MFPE. Our code is released at https://github.com/xx-adeline/MFPE.