Occluded person re-identification (re-ID) presents a challenging task due to occlusion perturbations. Although great efforts have been made to prevent the model from being disturbed by occlusion noise, most current solutions only capture information from a single image, disregarding the rich complementary information available in multiple images depicting the same pedestrian. In this paper, we propose a novel framework called Multi-view Information Integration and Propagation (MVI$^{2}$P). Specifically, realizing the potential of multi-view images in effectively characterizing the occluded target pedestrian, we integrate feature maps of which to create a comprehensive representation. During this process, to avoid introducing occlusion noise, we develop a CAMs-aware Localization module that selectively integrates information contributing to the identification. Additionally, considering the divergence in the discriminative nature of different images, we design a probability-aware Quantification module to emphatically integrate highly reliable information. Moreover, as multiple images with the same identity are not accessible in the testing stage, we devise an Information Propagation (IP) mechanism to distill knowledge from the comprehensive representation to that of a single occluded image. Extensive experiments and analyses have unequivocally demonstrated the effectiveness and superiority of the proposed MVI$^{2}$P. The code will be released at \url{https://github.com/nengdong96/MVIIP}.

翻译：遮挡行人重识别因遮挡扰动而极具挑战性。尽管现有工作已大量致力于防止模型受遮挡噪声干扰，但大多数方法仅从单张图像中捕获信息，忽略了同一行人的多视角图像中蕴含的丰富互补信息。本文提出一种名为多视角信息集成与传播（MVI$^{2}$P）的新型框架。具体而言，我们意识到多视角图像在有效表征被遮挡目标行人方面的潜力，通过集成其特征图构建综合表征。在此过程中，为避免引入遮挡噪声，我们开发了基于CAMs感知的定位模块，选择性集成有助于识别的信息。此外，考虑到不同图像判别性特征的差异，我们设计了概率感知量化模块，重点集成高可靠性信息。同时，由于测试阶段无法获取同一身份的多张图像，我们提出信息传播（IP）机制，将综合表征中的知识蒸馏至单张遮挡图像的表征中。大量实验与分析明确证明了所提MVI$^{2}$P的有效性与优越性。代码将在\url{https://github.com/nengdong96/MVIIP}开源。