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
this https URL

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

MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object  ReID

Human beings process stereoscopic correspondence across multiple scales.
However, this bio-inspiration is ignored by state-of-the-art cost aggregation
methods for dense stereo correspondence. In this paper, a generic cross-scale
cost aggregation framework is proposed to allow multi-scale interaction in cost
aggregation. We firstly reformulate cost aggregation from a unified
optimization perspective and show that different cost aggregation methods
essentially differ in the choices of similarity kernels. Then, an inter-scale
regularizer is introduced into optimization and solving this new optimization
problem leads to the proposed framework. Since the regularization term is
independent of the similarity kernel, various cost aggregation methods can be
integrated into the proposed general framework. We show that the cross-scale
framework is important as it effectively and efficiently expands
state-of-the-art cost aggregation methods and leads to significant
improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.

本文提出了一个通用的跨尺度成本聚合框架，以允许成本聚合的多尺度交互，并在多个数据集上得到了重大的改进。