Abstract
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 this https URL.
Abstract (translated)
遮挡的人重新识别（ReID）是一个非常具有挑战性的任务，由于遮挡干扰和缺乏目标信息，利用外部线索如人体姿态或解析来定位和对齐部分特征在遮挡的人重新识别中已经被证明非常有效。同时，最近使用的Transformer结构具有很强的长距离建模能力。考虑到上述事实，我们提出了一个教师-学生解码器（TSD）框架来进行遮挡的人重新识别，该框架利用了人类解析来辅助Transformer解码器。具体来说，我们提出的TSD由一个解析意识到的教师解码器（PTD）和一个标准学生解码器（SSD）组成。PTD利用人类解析线索来限制Transformer的注意力和传递信息给SSD通过特征蒸馏。因此，SSD可以从PTD中学到自动聚合身体部位的信息。此外，还设计了一个掩码生成器，用于提供更好的ReID。此外，现有的遮挡人重新识别基准采用遮挡样本作为查询，这会放大缓解遮挡干扰的作用，低估特征缺失问题的影响。相反，我们提出了一个新基准，作为现有基准的补充。大量实验证明，与我们的方法相比，我们的方法优越，新基准至关重要。源代码可以从该链接https://www.example.com/中获取。
URL
https://arxiv.org/abs/2312.09797
