学习实例自适应低秩正交子空间用于换衣行人重识别
Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification
AI总结：
提出Ortho-ReID方法，通过从VLM文本描述中显式建模低秩服装子空间，并利用几何约束提取服装不变特征，在多个基准数据集上取得最优性能。
摘要：
换衣行人重识别（CC-ReID）旨在识别尽管因服装变化导致外观剧烈变化的个体。现有方法依赖对抗学习来解耦服装特征，我们提出Ortho-ReID，该方法从VLM文本描述中显式建模低秩服装子空间，并通过直接几何约束提取服装不变表示。一个关键组件是基于Transformer的基生成器（Basis Maker），它通过与图像块的交叉注意力，将共享的低维服装先验细化为实例自适应低秩子空间，从而在变化的可见性条件下也能实现鲁棒的服装特征提取。该实例自适应子空间通过与服装文本嵌入对齐进行监督，而身份特征则通过可学习的投影头提取，并在几何上约束与其严格正交。大量实验表明，在PRCC（top-1提升5.9%）、Celeb-reID-light（提升3.5%）和LaST（提升5.3%）上达到了最先进性能，在LTCC上也取得了有竞争力的结果。
英文摘要：
Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation. While existing methods rely on adversarial learning to disentangle clothing features, we propose Ortho-ReID, which explicitly models a low-rank clothing subspace from VLM text descriptions and extracts clothing-invariant representations via direct geometric constraints. A critical component is our transformer-based Basis Maker, which refines a shared, low-dimensional clothing prior into an instance-adaptive low-rank subspace through cross-attention with image patches, enabling robust clothing feature extraction even under varying visibility conditions. This instance-adaptive subspace is supervised via alignment with clothing text embeddings, while identity features are extracted via a learnable projection head and geometrically constrained to be strictly orthogonal to it. Extensive experiments demonstrate state-of-the-art performance on PRCC (+5.9% top-1), Celeb-reID-light (+3.5%), and LaST (+5.3%), with competitive results on LTCC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
