Discriminative multi-scale adjacent feature for person re-identification

Recently, discriminative and robust identification information has played an increasingly critical role in Person Re-identification (Re-ID). It is a fact that the existing part-based methods demonstrate strong performance in the extraction of fine-grained features. However, their intensive partitions lead to semantic information ambiguity and background interference. Meanwhile, we observe that the body with different structural proportions. Hence, we assume that aggregation with the multi-scale adjacent features can effectively alleviate the above issues. In this paper, we propose a novel Discriminative Multi-scale Adjacent Feature (MSAF) learning framework to enrich semantic information and disregard background. In summary, we establish multi-scale interaction in two stages: the feature extraction stage and the feature aggregation stage. Firstly, a Multi-scale Feature Extraction (MFE) module is designed by combining CNN and Transformer structure to obtain the discriminative specific feature, as the basis for the feature aggregation stage. Secondly, a Jointly Part-based Feature Aggregation (JPFA) mechanism is revealed to implement adjacent feature aggregation with diverse scales. The JPFA contains Same-scale Feature Correlation (SFC) and Cross-scale Feature Correlation (CFC) sub-modules. Finally, to verify the effectiveness of the proposed method, extensive experiments are performed on the common datasets of Market-1501, CUHK03-NP, DukeMTMC, and MSMT17. The experimental results achieve better performance than many state-of-the-art methods.