Unsupervised Domain Adaptation Person Re-Identification: Bridged by Feature Fusion Transitional Domain
Qing Tian, Xiang Liu, Jixin Sun, Jun Wan, Zhen Lei · IEEE Transactions on Information Forensics and Security · 2025
The goal of unsupervised domain adaptation person re-identification (UDA Reid) is to achieve feature space alignment between the source domain and the target domain, so that the Reid model can effectively match pedestrians in the target domain. Creating the transitional domain is an effective approach, but existing models often have difficulty synthesizing transitional domains with sufficiently public features. To tackle this challenge, we propose an innovative approach named feature fusion transitional domain (F2TD-Reid), which comprises two essential components: the dictionary fusion module (DFM) and the transitional domain attention module (TDAM). Among them, the DFM utilizes a feature fusion to extract and reconstruct pedestrian images from instances, focusing on capturing the essential visual elements within the images. For the TDAM, it further refines the feature extraction of instance points through an innovative weighted attention mechanism. These two modules optimize the generation process of scaling factors, thereby facilitating the transfer of knowledge between the source domain and the target domain. Through a series of comparative experiments, we verify the superiority of the F2TD-Reid method in solving UDA Reid. The code is available at https://github.com/1x-x/F2TD-Reid.