Causal Feature Supervision Decoupling: A Novel Method for Clothes-Changing Person Re-identification Algorithm

Clothes-Changing person re-identification algorithm (Re-ID) is the task of retrieving the query person in the case of the change of pedestrian clothing. Changes in pedestrian clothing lead to an offset in clothing features, resulting in a decrease in identification performance. Simply removing clothing may lead to the loss of contour information. Furthermore, algorithms based on feature decoupling cannot guarantee the accuracy of the positional relationship between clothing features and other features due to the lack of groundtruth. To address these issues, we propose a novel clothes-changing person re-identification algorithm based on causal feature supervision decoupling. Utilizing a multi-scale feature fusion module to extract fine-grained features of clothing and add supervised information labels. This enables the dual-branch network to separately approach overall and clothing features, promoting the extraction of effective identity information by the causal decoupling module, and obtaining unbiased estimations of pedestrians. Experimental results show that the proposed algorithm achieves the highest mAP and Top-1 accuracy on the LTCC and PRCC datasets. The source code is available at https://github.com/zhihu250/CISupNet.