X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification

Currently, most existing person re-identification methods use instance-level features, which are extracted only from a single image. However, these instance-level features can easily ignore the discriminative information because the appearance of each identity varies greatly in different images. Thus, it is necessary to exploit identity-level features, which can be shared across different images of each identity. In this paper, we propose a novel training framework, named X-ReID, to promote instance-level features to identity-level features by employing cross-attention to incorporate information from one image to another of the same identity, thus more unified and discriminative pedestrian information can be obtained. Extensive experiments on benchmark datasets show the superiority of our method over existing works. Particularly, on the challenging MSMT17, our proposed method gains 1.1% mAP improvements when compared to the second place.