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

• URL: http://arxiv.org/abs/2302.02075v1
• Date: Sat, 4 Feb 2023 03:16:18 GMT
• Title: X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification
• Authors: Leqi Shen, Tao He, Yuchen Guo, Guiguang Ding
• Abstract summary: Cross Intra-Identity Instances module (IntraX) fuses different intra-identity instances to transfer Identity-Level knowledge. Cross Inter-Identity Instances module (InterX) involves hard positive and hard negative instances to improve the attention response to the same identity.
• Score: 53.047542904329866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 due to 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 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. We propose a novel training framework named X-ReID. Specifically, a Cross Intra-Identity Instances module (IntraX) fuses different intra-identity instances to transfer Identity-Level knowledge and make Instance-Level features more compact. A Cross Inter-Identity Instances module (InterX) involves hard positive and hard negative instances to improve the attention response to the same identity instead of different identity, which minimizes intra-identity variation and maximizes inter-identity variation. 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.

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