Occluded person re-identification (Re-ID) is a challenging task as persons
are frequently occluded by various obstacles or other persons, especially in
the crowd scenario. To address these issues, we propose a novel end-to-end
Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part
discovery via a transformer encoderdecoder architecture, including a pixel
context based transformer encoder and a part prototype based transformer
decoder. The proposed PAT model enjoys several merits. First, to the best of
our knowledge, this is the first work to exploit the transformer
encoder-decoder architecture for occluded person Re-ID in a unified deep model.
Second, to learn part prototypes well with only identity labels, we design two
effective mechanisms including part diversity and part discriminability.
Consequently, we can achieve diverse part discovery for occluded person Re-ID
in a weakly supervised manner. Extensive experimental results on six
challenging benchmarks for three tasks (occluded, partial and holistic Re-ID)
demonstrate that our proposed PAT performs favorably against stat-of-the-art
methods.

通过基于变压器编解码器架构的多元部分发现，包括基于像素上下文的变压器编码器和基于部分原型的变压器解码器，我们提出了一种新的端对端的面向部分的变压器，用于处理拥挤情景中受遮挡的人物重识别。实验结果表明，提出的方法在三个任务（受遮挡，部分和整体重识别）的六项基准测试中表现良好。