Occluded person re-identification (ReID) is a very challenging task due to the occlusion disturbance and incomplete target information. Leveraging external cues such as human pose or parsing to locate and align part features has been proven to be very effective in occluded person ReID. Meanwhile, recent Transformer structures have a strong ability of long-range modeling. Considering the above facts, we propose a Teacher-Student Decoder (TSD) framework for occluded person ReID, which utilizes the Transformer decoder with the help of human parsing. More specifically, our proposed TSD consists of a Parsing-aware Teacher Decoder (PTD) and a Standard Student Decoder (SSD). PTD employs human parsing cues to restrict Transformer's attention and imparts this information to SSD through feature distillation. Thereby, SSD can learn from PTD to aggregate information of body parts automatically. Moreover, a mask generator is designed to provide discriminative regions for better ReID. In addition, existing occluded person ReID benchmarks utilize occluded samples as queries, which will amplify the role of alleviating occlusion interference and underestimate the impact of the feature absence issue. Contrastively, we propose a new benchmark with non-occluded queries, serving as a complement to the existing benchmark. Extensive experiments demonstrate that our proposed method is superior and the new benchmark is essential. The source codes are available at https://github.com/hh23333/TSD.

翻译：遮挡行人重识别（ReID）因遮挡干扰和不完整目标信息而极具挑战性。利用人体姿态或人体解析等外部线索来定位和对齐部件特征，已被证明在遮挡行人重识别中非常有效。同时，近期Transformer结构具备强大的长程建模能力。基于上述事实，本文提出一种面向遮挡行人重识别的教师-学生解码器（TSD）框架，该框架借助人体解析信息，利用Transformer解码器实现功能。具体而言，我们提出的TSD包含一个解析感知教师解码器（PTD）和标准学生解码器（SSD）。PTD利用人体解析线索约束Transformer的注意力，并通过特征蒸馏将该信息传递至SSD。由此，SSD能够向PTD学习，自动聚合身体部件信息。此外，我们设计了一个掩码生成器，用于提供更具判别性的区域以提升重识别性能。同时，现有遮挡行人重识别基准使用遮挡样本作为查询，这会放大缓解遮挡干扰的作用，而低估特征缺失问题的影响。相比之下，我们提出一个采用非遮挡查询的新基准，作为现有基准的补充。大量实验表明，我们提出的方法具有优越性，且新基准不可或缺。源代码已公开于https://github.com/hh23333/TSD。