Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance. Previous works explore new optimizing targets and search spaces for NAS ReID, yet they neglect the difference of training schemes between image classification and ReID. In this work, we propose a novel Twins Contrastive Mechanism (TCM) to provide more appropriate supervision for ReID architecture search. TCM reduces the category overlaps between the training and validation data, and assists NAS in simulating real-world ReID training schemes. We then design a Multi-Scale Interaction (MSI) search space to search for rational interaction operations between multi-scale features. In addition, we introduce a Spatial Alignment Module (SAM) to further enhance the attention consistency confronted with images from different sources. Under the proposed NAS scheme, a specific architecture is automatically searched, named as MSINet. Extensive experiments demonstrate that our method surpasses state-of-the-art ReID methods on both in-domain and cross-domain scenarios. Source code available in https://github.com/vimar-gu/MSINet.

该研究提出了一个新颖的双胞胎对比机制（TCM）和一个多尺度交互（MSI）搜索空间来提高ReID体系结构的搜索，引入空间对齐模块（SAM）来增强不同来源图像的注意力一致性，并展示了一种名为MSINet的自动搜索出的特定结构，在领域内和领域间场景下均优于最先进的ReID方法。

MSINet: 基于孪生对比搜索的多尺度交互物体重识别