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.

最近，具有区分性和鲁棒性的识别信息在行人重新识别（Re-ID）中发挥着越来越重要的作用。事实是，现有的基于部分的方法在提取细粒度特征方面表现出了很强的性能。然而，它们的密集分区导致语义信息模糊和背景干扰。同时，我们观察到身体具有不同的结构比例。因此，我们假设多尺度相邻特征的聚合可以有效缓解上述问题。在本文中，我们提出了一种新颖的判别性多尺度相邻特征（MSAF）学习框架来丰富语义信息并忽略背景。总之，我们分两个阶段建立多尺度交互：特征提取阶段和特征聚合阶段。首先，结合CNN和Transformer结构设计多尺度特征提取（MFE）模块，以获得具有判别性的特定特征，作为特征聚合阶段的基础。其次，提出了一种基于联合部分的特征聚合（JPFA）机制来实现不同尺度的相邻特征聚合。 JPFA 包含同尺度特征关联 (SFC) 和跨尺度特征关联 (CFC) 子模块。最后，为了验证所提出方法的有效性，在Market-1501、CUHK03-NP、DukeMTMC和MSMT17的常见数据集上进行了广泛的实验。实验结果比许多最先进的方法取得了更好的性能。