  One major issue that challenges person re-identification (Re-ID) is the
ubiquitous occlusion over the captured persons. There are two main challenges
for the occluded person Re-ID problem, i.e., the interference of noise during
feature matching and the loss of pedestrian information brought by the
occlusions. In this paper, we propose a new approach called Feature Recovery
Transformer (FRT) to address the two challenges simultaneously, which mainly
consists of visibility graph matching and feature recovery transformer. To
reduce the interference of the noise during feature matching, we mainly focus
on visible regions that appear in both images and develop a visibility graph to
calculate the similarity. In terms of the second challenge, based on the
developed graph similarity, for each query image, we propose a recovery
transformer that exploits the feature sets of its $k$-nearest neighbors in the
gallery to recover the complete features. Extensive experiments across
different person Re-ID datasets, including occluded, partial and holistic
datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly
outperforms state-of-the-art results by at least 6.2\% Rank-1 accuracy and
7.2\% mAP scores on the challenging Occluded-Duke dataset. The code is
available at https://github.com/xbq1994/Feature-Recovery-Transformer.
