  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.
