  Most existing methods tackle the problem of occluded person re-identification
(ReID) by utilizing auxiliary models, resulting in a complicated and
inefficient ReID framework that is unacceptable for real-time applications. In
this work, a speed-up person ReID framework named SUReID is proposed to
mitigate occlusion interference while speeding up inference. The SUReID
consists of three key components: hierarchical token sparsification (HTS)
strategy, non-parametric feature alignment knowledge distillation (NPKD), and
noise occlusion data augmentation (NODA). The HTS strategy works by pruning the
redundant tokens in the vision transformer to achieve highly effective
self-attention computation and eliminate interference from occlusions or
background noise. However, the pruned tokens may contain human part features
that contaminate the feature representation and degrade the performance. To
solve this problem, the NPKD is employed to supervise the HTS strategy,
retaining more discriminative tokens and discarding meaningless ones.
Furthermore, the NODA is designed to introduce more noisy samples, which
further trains the ability of the HTS to disentangle different tokens.
Experimental results show that the SUReID achieves superior performance with
surprisingly fast inference.
