QA-ReID: Quality-Aware Query-Adaptive Convolution Leveraging Fused Global and Structural Cues for Clothes-Changing ReID

TL;DR

This paper introduces QA-ReID, a novel approach for clothes-changing person re-identification that combines global and structural features with adaptive fusion and quality-aware convolution to improve robustness and accuracy.

Contribution

The paper proposes a new dual-branch framework with adaptive feature fusion and a quality-aware convolution for clothes-changing ReID, advancing state-of-the-art performance.

Findings

Achieves state-of-the-art results on PRCC, LTCC, and VC-Clothes benchmarks.

Significantly outperforms existing methods in cross-clothing scenarios.

Demonstrates robustness against appearance variations caused by clothing changes.

Abstract

Unlike conventional person re-identification (ReID), clothes-changing ReID (CC-ReID) presents severe challenges due to substantial appearance variations introduced by clothing changes. In this work, we propose the Quality-Aware Dual-Branch Matching (QA-ReID), which jointly leverages RGB-based features and parsing-based representations to model both global appearance and clothing-invariant structural cues. These heterogeneous features are adaptively fused through a multi-modal attention module. At the matching stage, we further design the Quality-Aware Query Adaptive Convolution (QAConv-QA), which incorporates pixel-level importance weighting and bidirectional consistency constraints to enhance robustness against clothing variations. Extensive experiments demonstrate that QA-ReID achieves state-of-the-art performance on multiple benchmarks, including PRCC, LTCC, and VC-Clothes, and significantly outperforms existing approaches under cross-clothing scenarios.