Zhang Haoyu, Liu Meng, Li Yuhong, Yan Ming, Gao Zan, Chang Xiaojun, Nie Liqiang
IEEE Trans Pattern Anal Mach Intell. 2023 Dec;45(12):14144-14160. doi: 10.1109/TPAMI.2023.3312302. Epub 2023 Nov 3.
Partial person re-identification (ReID) aims to solve the problem of image spatial misalignment due to occlusions or out-of-views. Despite significant progress through the introduction of additional information, such as human pose landmarks, mask maps, and spatial information, partial person ReID remains challenging due to noisy keypoints and impressionable pedestrian representations. To address these issues, we propose a unified attribute-guided collaborative learning scheme for partial person ReID. Specifically, we introduce an adaptive threshold-guided masked graph convolutional network that can dynamically remove untrustworthy edges to suppress the diffusion of noisy keypoints. Furthermore, we incorporate human attributes and devise a cyclic heterogeneous graph convolutional network to effectively fuse cross-modal pedestrian information through intra- and inter-graph interaction, resulting in robust pedestrian representations. Finally, to enhance keypoint representation learning, we design a novel part-based similarity constraint based on the axisymmetric characteristic of the human body. Extensive experiments on multiple public datasets have shown that our model achieves superior performance compared to other state-of-the-art baselines.
部分行人重识别（ReID）旨在解决由于遮挡或视角外而导致的图像空间错位问题。尽管通过引入额外信息（如人体姿态地标、掩码图和空间信息）取得了显著进展，但由于关键点噪声和易受影响的行人表示，部分行人ReID仍然具有挑战性。为了解决这些问题，我们提出了一种用于部分行人ReID的统一属性引导协作学习方案。具体而言，我们引入了一种自适应阈值引导的掩码图卷积网络，该网络可以动态去除不可信的边以抑制噪声关键点的扩散。此外，我们纳入了人体属性，并设计了一个循环异构图卷积网络，通过图内和图间交互有效地融合跨模态行人信息，从而得到鲁棒的行人表示。最后，为了增强关键点表示学习，我们基于人体的轴对称特性设计了一种新颖的基于部分的相似性约束。在多个公共数据集上进行广泛实验表明，与其他现有最先进的基线相比，我们的模型取得了卓越的性能。
