  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}.
