Attention-calibration based double-branch cross-domain person re-identification

Existing cross-domain pedestrian re-identification methods tend to utilize domain adaptation or domain generalization strategies to eliminate the differences between domains, but these methods fail to fully characterize the characteristics of unknown domain samples, resulting in poor recognition performance in unknown domain. By learning specific features and invariant features of different domains, this paper proposes a new attention-calibration double-branch cross-domain pedestrian re -identification network (ACDBNet). Firstly, the attention calibration module is utilized to extract the channel information and spatial information of an image, and locate the distinguishable local features; then, the global branch and local branch are introduced to fully learn the domain specific feature and domain invariant feature respectively, and the features in these two branches are linearly weighted fused. Experimental results on a large number of public datasets demonstrate that the proposed method has excellent performance in various environments.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a new attention-calibration double-branch cross-domain pedestrian re-identification network, which achieves excellent performance in various environments by learning features from different domains and fusing them.