Unsupervised Multi-source Adaptive Pedestrian Re-recognition: Based on Target Domain Prioritization and Multi-dimensional Edge Features

Unsupervised multi-source domain adaptation facilitates the transfer of knowledge from multiple source domains, which possess labeled data, to an unlabeled target domain. Pedestrian re-identification is a technique for cross-camera pedestrian retrieval in surveillance data. The utilization of multiple source domains holds significant research implications, particularly in scenarios involving a substantial volume of data. Recently, efforts have been made to eliminate distributional differences between data from different domains. However, these approaches do not take into account the specificity of the target domain. Furthermore, when using graph convolutional networks for domain fusion, few studies have explored the utilization of deep correlations between nodes, which is crucial for node updates in GCNs. In this paper, we present a novel methodology that enhances domain fusion and showcases robust performance. In particular, we first propose a Domain Fusion Module based on the prioritization of target domains, which enables the fusion of domain information. Second, in order to mine the deep correlation between nodes, we process it by introducing multidimensional edge features. Our multiple experimental results using four datasets on three migration tasks demonstrate the superior performance of the DFTM, thus providing strong support for the effectiveness of our approach.