Occluded person re-identification (ReID) isa challenging task as the body is partially occluded by obstacles in crowd scenarios. Many previous studies employed an attention mechanism to focus on fine-grained local infor-mation with conventional likelihood while ignoring the inherent causality between the final prediction results and attention, especially occluded person always possesses biased clues. To address this problem, we propose a Pose-Guided Multi-Attention Network (PGMA-Net) for occluded person ReID in an end-to-end manner. PGMA-Net contains two main novel components: Pose-Guided Counterfactual Inference Branch (PGCIB) and Striped-and Patched-Attention Module (SPAM). The PGCIB jointly explores the causality between the predicted identities and input clues to alleviate the negative effects brought by occluded bias. Specifically, the counterfac-tual inference can directly guide the attention learning process via the counterfactual intervention. The SPAM generates a set of attention vectors for storing part prototypes over multiple rounds of attention. We empirically demonstrate that PGMA-Net can improve the recognition in both occluded and non-occluded ReID. With the above designs, our framework achieves 52.2 in mAP and 62.0 in top-1 on the Occluded-DukeMTMC dataset, surpassing the baseline by a large margin. (c) 2022 Elsevier B.V. All rights reserved.
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