  Cloth-changing person reidentification (ReID) is a newly emerging research
topic that aims to retrieve pedestrians whose clothes are changed. Since the
human appearance with different clothes exhibits large variations, it is very
difficult for existing approaches to extract discriminative and robust feature
representations. Current works mainly focus on body shape or contour sketches,
but the human semantic information and the potential consistency of pedestrian
features before and after changing clothes are not fully explored or are
ignored. To solve these issues, in this work, a novel semantic-aware attention
and visual shielding network for cloth-changing person ReID (abbreviated as
SAVS) is proposed where the key idea is to shield clues related to the
appearance of clothes and only focus on visual semantic information that is not
sensitive to view/posture changes. Specifically, a visual semantic encoder is
first employed to locate the human body and clothing regions based on human
semantic segmentation information. Then, a human semantic attention module
(HSA) is proposed to highlight the human semantic information and reweight the
visual feature map. In addition, a visual clothes shielding module (VCS) is
also designed to extract a more robust feature representation for the
cloth-changing task by covering the clothing regions and focusing the model on
the visual semantic information unrelated to the clothes. Most importantly,
these two modules are jointly explored in an end-to-end unified framework.
Extensive experiments demonstrate that the proposed method can significantly
outperform state-of-the-art methods, and more robust features can be extracted
for cloth-changing persons. Compared with FSAM (published in CVPR 2021), this
method can achieve improvements of 32.7% (16.5%) and 14.9% (-) on the LTCC and
PRCC datasets in terms of mAP (rank-1), respectively.
