Exact Feature Distribution Matching and Multi-domain Information Fusion for Person Re-identification

‍ ‍The task of unsupervised domain adaptation for person re-identification （UDA-ReID） is to transfer knowledge from the marked source domain to the unmarked target domain. Compared with the traditional single-source domain adaptation， it is a more challenging task to transfer multi-source domain knowledge to the target domain. Due to the gap between different domains， the simple combination of multiple datasets can only be improved limited. To solve this problem， a multi-domain contrastive learning method based on exact feature distribution matching and multi-domain information fusion （MFMCL） was proposed. This method firstly extracted the features of data in different domains based on the self-paced contrastive learning with hybrid memory， and constructed the knowledge graph based on the extracted features， and then fused the multi-domain features through the two-layer residual graph convolution network. In addition， in order to enhance the cross distribution features between different domains and generate more abundant image style information， the exact feature distribution matching was realized by accurate histogram matching based on sorting algorithm， so as to obtain more diversified feature enhancement. Compared with the advanced UDA-ReID methods， the experimental results show that our proposed MFMCL method achieves the best performance on the widely used ReID datasets Market1501， MSMT17 and Duke.