Contrastive Pedestrian Attentive and Correlation Learning Network for Occluded Person Re-Identification

Occluded person Re-identification (ReID) aims to match occluded and holistic pedestrian images across different camera views. This task presents two primary challenges. First, it is crucial to accurately capture pedestrian foregrounds from seriously occluded person images. Second, a noticeable information asymmetry exists between the partial body in occluded images and the complete body in corresponding holistic images, which could cause the ReID model to underestimate their similarities. To address these challenges, we introduce a contrastive pedestrian attentive and correlation learning (CpaCol) model. Within CpaCol, we first design a Contrastive Pedestrian Attention (ContrastAttn) module to capture pedestrian foregrounds from occluded images. In this process, we notice that most existing attention-based methods only supervise the final predictions with identity loss yet neglect its causality with the generated attention maps, which could mislead the model to capture some salient yet pedestrian-irrelevant noises as discriminative clues. To rectify this, we integrate contrastive learning into our ContrastAttn module to guide it to learn the semantic divergence between pedestrian foregrounds and noises, thereby capturing pedestrian foregrounds more accurately. Besides, we propose a correlation learning module, where we tailor an effective dense feature correlation learning tool, 4D convolution, to enable it to adapt to pedestrian images and capture corresponding clues between comparing images. By focusing more on corresponding clues, our model could avoid overemphasizing the inherent information asymmetry between occluded and holistic images, thereby improving re-identification. Empowered by these modules, our CpaCol achieves state-of-the-art performance on three relevant ReID settings, i.e., occluded, partial, and holistic ReID. Our code is available in https://github.com/nwpugaoliying/CpaCol.