Abstract
Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are usually built upon the ResNet or VGG backbones, which were originally designed for classification. Because reID has certain differences from classification, the architecture should be modified accordingly. We propose to search for a CNN architecture that is specifically suitable for the reID task. There are three main problems. First, body structural information plays an important role in reID but is not encoded in backbones. Part-based reID models incorporate structure information at the tail of a CNN. Performance relies heavily on human experts and the models are backbone-dependent, requiring extensive human effort when a different backbone is used. Second, Neural Architecture Search (NAS) automates the process of architecture design without human effort, but no existing NAS methods incorporate the structure information of input images. Third, reID is essentially a retrieval task but current NAS algorithms are merely designed for classification. To solve these problems, we propose a retrieval-based search algorithm over a specifically designed reID search space, named Auto-ReID. Our Auto-ReID enables the automated approach to find an efficient and effective CNN architecture that is specifically suitable for reID. Extensive experiments indicate that the searched architecture achieves state-of-the-art performance while requiring less than about 50% parameters and 53% FLOPs compared to others.
Abstract (translated)
目前流行的深卷积神经网络（CNN）用于人的再识别（REID），通常建立在resnet或vgg主干上，它们最初是为分类而设计的。由于REID与分类有一定的区别，因此应该对体系结构进行相应的修改。我们建议寻找一个CNN架构，特别适合REID任务。主要有三个问题。首先，身体结构信息在REID中起着重要作用，但不编码在主干中。基于部分的REID模型在CNN的尾部包含结构信息。性能严重依赖于人类专家，模型依赖于主干，当使用不同的主干时，需要大量的人力。第二，神经架构搜索（nas）在不需要人工的情况下自动完成架构设计过程，但是现有的nas方法都没有将输入图像的结构信息结合起来。第三，REID本质上是一个检索任务，但当前的NAS算法只是为分类而设计的。为了解决这些问题，我们提出了一个基于检索的搜索算法，在一个专门设计的REID搜索空间，即自动REID。我们的auto-reid使自动化方法能够找到一个高效和有效的CNN架构，特别适合reid。大量的实验表明，搜索的体系结构达到了最先进的性能，但与其他体系结构相比，只需要不到50%的参数和53%的触发器。
URL
https://arxiv.org/abs/1903.09776
