  Occluded person re-identification (ReID) aims at matching occluded person
images to holistic ones across different camera views. Target Pedestrians (TP)
are usually disturbed by Non-Pedestrian Occlusions (NPO) and NonTarget
Pedestrians (NTP). Previous methods mainly focus on increasing model's
robustness against NPO while ignoring feature contamination from NTP. In this
paper, we propose a novel Feature Erasing and Diffusion Network (FED) to
simultaneously handle NPO and NTP. Specifically, NPO features are eliminated by
our proposed Occlusion Erasing Module (OEM), aided by the NPO augmentation
strategy which simulates NPO on holistic pedestrian images and generates
precise occlusion masks. Subsequently, we Subsequently, we diffuse the
pedestrian representations with other memorized features to synthesize NTP
characteristics in the feature space which is achieved by a novel Feature
Diffusion Module (FDM) through a learnable cross attention mechanism. With the
guidance of the occlusion scores from OEM, the feature diffusion process is
mainly conducted on visible body parts, which guarantees the quality of the
synthesized NTP characteristics. By jointly optimizing OEM and FDM in our
proposed FED network, we can greatly improve the model's perception ability
towards TP and alleviate the influence of NPO and NTP. Furthermore, the
proposed FDM only works as an auxiliary module for training and will be
discarded in the inference phase, thus introducing little inference
computational overhead. Experiments on occluded and holistic person ReID
benchmarks demonstrate the superiority of FED over state-of-the-arts, where FED
achieves 86.3% Rank-1 accuracy on Occluded-REID, surpassing others by at least
4.7%.
