Abstract
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.
Abstract (translated)
神经网络结构搜索(NAS)越来越吸引对象重新识别(ReID)社会的关注,因为任务特定的架构 significantly 改善检索性能。以前的工作探索了NAS ReID新的优化目标和搜索空间,但它们忽略了图像分类和ReID之间的训练计划差异。在本文中,我们提出了一种全新的双工比较机制(TCM),以提供更适当的监督ReID架构搜索。TCM减少了训练和验证数据之间的类别重叠,并协助NAS模拟真实的ReID训练计划。随后,我们设计了一个多尺度交互(MSI)搜索空间,以搜索多尺度特征之间的合理交互操作。此外,我们引入了一个空间定位模块(SAM),以进一步加强面对来自不同来源的图像的注意力一致性。在所提出的NAS计划下,一种特定的架构被自动搜索,称为MSINet。广泛的实验表明,我们的方法在跨域和域内场景上超越了最先进的ReID方法。源代码可在本 https URL 上获取。
URL
https://arxiv.org/abs/2303.07065
