  The attention mechanism is widely used in deep learning because of its
excellent performance in neural networks without introducing additional
information. However, in unsupervised person re-identification, the attention
module represented by multi-headed self-attention suffers from attention
spreading in the condition of non-ground truth. To solve this problem, we
design pixel-level attention module to provide constraints for multi-headed
self-attention. Meanwhile, for the trait that the identification targets of
person re-identification data are all pedestrians in the samples, we design
domain-level attention module to provide more comprehensive pedestrian
features. We combine head-level, pixel-level and domain-level attention to
propose multi-level attention block and validate its performance on for large
person re-identification datasets (Market-1501, DukeMTMC-reID and MSMT17 and
PersonX).
