Part-Attention Based Model Make Occluded Person Re-Identification Stronger

• URL: http://arxiv.org/abs/2404.03443v4
• Date: Wed, 1 May 2024 08:39:50 GMT
• Title: Part-Attention Based Model Make Occluded Person Re-Identification Stronger
• Authors: Zhihao Chen, Yiyuan Ge,
• Abstract summary: We introduce PAB-ReID, which is a novel ReID model incorporating part-attention mechanisms to tackle the issues effectively. Firstly, we introduce the human parsing label to guide the generation of more accurate human part attention maps. In addition, we propose a fine-grained feature focuser for generating fine-grained human local feature representations while suppressing background interference.
• Score: 1.7648680700685022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of occluded person re-identification (ReID) is to retrieve specific pedestrians in occluded situations. However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance. In our research, we introduce a new framework called PAB-ReID, which is a novel ReID model incorporating part-attention mechanisms to tackle the aforementioned issues effectively. Firstly, we introduce the human parsing label to guide the generation of more accurate human part attention maps. In addition, we propose a fine-grained feature focuser for generating fine-grained human local feature representations while suppressing background interference. Moreover, We also design a part triplet loss to supervise the learning of human local features, which optimizes intra/inter-class distance. We conducted extensive experiments on specialized occlusion and regular ReID datasets, showcasing that our approach outperforms the existing state-of-the-art methods.

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