针对换装行人重识别，融合外观与结构信息提升匹配鲁棒性。
QA-ReID: Quality-Aware Query-Adaptive Convolution Leveraging Fused Global and Structural Cues for Clothes-Changing ReID
- 双分支融合RGB特征与分割特征，建模全局外观与不变结构。
- 在多个数据集上达到当前最优，跨服装场景性能显著领先。
- 适合关注换装场景下行人重识别的科研与应用开发者。
与传统行人重识别不同，换装行人重识别（CC-ReID）因服装变化带来的显著外观差异而面临严峻挑战。本文提出质量感知双分支匹配框架QA-ReID，联合利用基于RGB的特征与基于分割的表征，建模全局外观与衣物不变的结构线索。这些异构特征通过多模态注意力模块自适应融合。在匹配阶段，进一步设计质量感知查询自适应卷积（QAConv-QA），引入像素级重要性加权与双向一致性约束，增强对服装变化的鲁棒性。大量实验表明，QA-ReID在PRCC、LTCC和VC-Clothes等多个基准上均达到领先性能，在跨服装场景下显著优于现有方法。
原文摘要 · Abstract (English)
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.
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