Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relevant features. However, we observe that relying solely on clothes-irrelevant features for clothes-changing re-id is limited, since they often lack adequate identity information and suffer from large intra-class variations. On the contrary, clothes-relevant features can be used to discover same-clothes intermediaries that possess informative identity clues. Based on this observation, we propose a Feasibility-Aware Intermediary Matching (FAIM) framework to additionally utilize clothes-relevant features for retrieval. Firstly, an Intermediary Matching (IM) module is designed to perform an intermediary-assisted matching process. This process involves using clothes-relevant features to find informative intermediates, and then using clothes-irrelevant features of these intermediates to complete the matching. Secondly, in order to reduce the negative effect of low-quality intermediaries, an Intermediary-Based Feasibility Weighting (IBFW) module is designed to evaluate the feasibility of intermediary matching process by assessing the quality of intermediaries. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on several widely-used clothes-changing re-id benchmarks.

翻译：当前换装行人重识别方法通常基于服装无关特征进行检索，而忽视了服装相关特征的潜力。然而，我们观察到仅依赖服装无关特征进行换装重识别存在局限，因为这些特征往往缺乏足够的身份信息且存在较大的类内差异。相反，服装相关特征可用于发现具有信息性身份线索的同服装中间体。基于这一观察，我们提出了可行性感知中间体匹配框架，以额外利用服装相关特征进行检索。首先，设计了中间体匹配模块来执行中间体辅助的匹配过程：该过程利用服装相关特征寻找信息性中间体，随后通过这些中间体的服装无关特征完成匹配。其次，为降低低质量中间体的负面影响，设计了基于中间体的可行性加权模块，通过评估中间体质量来衡量中间体匹配过程的可行性。大量实验表明，我们的方法在多个广泛使用的换装重识别基准数据集上优于现有最优方法。