  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.
