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.

我们在研究中引入了一种名为 PAB-ReID 的新框架，该框架采用部分注意机制来有效解决背景干扰和低质量本地特征表示等问题，通过引入人体分割标签来生成更准确的人体部分注意力图，提出了一种细粒度特征聚焦器，以生成精细的人体局部特征表示，并抑制背景干扰，同时设计了部分三元损失来监督人体局部特征的学习，优化了类内 / 类间距离，大量实验证明我们的方法胜过现有的最先进方法。