  The goal of person search is to localize a target person from a gallery set
of scene images, which is extremely challenging due to large scale variations,
pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade
Occluded Attention Transformer (COAT) for end-to-end person search. Our
three-stage cascade design focuses on detecting people in the first stage,
while later stages simultaneously and progressively refine the representation
for person detection and re-identification. At each stage the occluded
attention transformer applies tighter intersection over union thresholds,
forcing the network to learn coarse-to-fine pose/scale invariant features.
Meanwhile, we calculate each detection's occluded attention to differentiate a
person's tokens from other people or the background. In this way, we simulate
the effect of other objects occluding a person of interest at the token-level.
Through comprehensive experiments, we demonstrate the benefits of our method by
achieving state-of-the-art performance on two benchmark datasets.
