Attribute Guidance With Inherent Pseudo-label For Occluded Person Re-identification

TL;DR

This paper introduces AG-ReID, a framework that leverages pre-trained models to extract fine-grained attributes for occluded person re-identification, significantly improving accuracy without extra annotations.

Contribution

The paper proposes a novel two-stage attribute-guided framework that uses inherent pseudo-labels from pre-trained models to enhance occluded person Re-ID performance.

Findings

Achieves state-of-the-art results on multiple datasets.

Effectively handles occlusions and subtle attribute differences.

Maintains competitive performance on standard Re-ID tasks.

Abstract

Person re-identification (Re-ID) aims to match person images across different camera views, with occluded Re-ID addressing scenarios where pedestrians are partially visible. While pre-trained vision-language models have shown effectiveness in Re-ID tasks, they face significant challenges in occluded scenarios by focusing on holistic image semantics while neglecting fine-grained attribute information. This limitation becomes particularly evident when dealing with partially occluded pedestrians or when distinguishing between individuals with subtle appearance differences. To address this limitation, we propose Attribute-Guide ReID (AG-ReID), a novel framework that leverages pre-trained models' inherent capabilities to extract fine-grained semantic attributes without additional data or annotations. Our framework operates through a two-stage process: first generating attribute pseudo-labels that capture subtle visual characteristics, then introducing a dual-guidance mechanism that combines holistic and fine-grained attribute information to enhance image feature extraction. Extensive experiments demonstrate that AG-ReID achieves state-of-the-art results on multiple widely-used Re-ID datasets, showing significant improvements in handling occlusions and subtle attribute differences while maintaining competitive performance on standard Re-ID scenarios.