Part-Attention Based Model Make Occluded Person Re-Identification Stronger
<|reference_start|>Part-Attention Based Model Make Occluded Person Re-Identification Stronger: 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.<|reference_end|>
@article{chen2024part-attention,
    title={Part-Attention Based Model Make Occluded Person Re-Identification
  Stronger},
    author={Zhihao Chen and Yiyuan Ge},
    journal={arXiv preprint arXiv:2404.03443},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03443},
    primaryClass={cs.CV}
}