多维注意力与部件关注的无监督行人重识别
Unsupervised Person Re-Identification Based on Multi-Dimensional Attention and Part Focus Network
摘要: 行人重识别的目的是通过将人物的探测图像与图像库中的所有图像进行比较，从而在图像库中找到感兴趣的人。大多数的行人重识别算法都是在一些小的带标签的数据集上进行监督训练，直接将这些训练好的模型部署到真实世界的大型摄像机网络中可能会由于拟合不足而导致性能低下。因此，有必要在没有明确监督的情况下，自主地对模型进行训练。因此本文提出了一个多维注意力网络和部件关注网络联合学习的无监督行人重识别方法。首先多维注意力网络对行人图像复杂的高阶统计信息进行建模和利用，其次使用部件关注网络关注不同的部件，最后是一系列的损失函数来引导部件关注网络学习未标记数据集上的部件特征。在Market-1501和DukeMTMC-reID两个数据集上的实验结果表明，本文提出的方法有效并取得了显著的效果。
Abstract: Person re-identification (Re-ID) aims at finding a person of interest in the image gallery by comparing the probe image of this person with all the gallery images. Most of the Re-ID algorithms conduct supervised training in some small labeled datasets, so directly deploying these trained models to the real-world large camera networks may lead to a poor performance due to underfitting. Therefore, it is necessary to train models without explicit supervision in an autonomous manner, and propose an unsupervised Re-ID method based on Multi-dimensional Attention Network (MDAN) and Part Focus Network (PFN). MDAN can model and utilize the complex higher-order statistics in-formation in attention mechanism, so as to capture the subtle differences among pedestrians and to produce the discriminative attention proposals. Then there is a PFN, which is deployed into an improved spatial transform network (STN) so that each branch can focus on different parts of the pedestrian. We evaluate the proposed method on two public datasets, including Market-1501 and DukeMTMC-reID. Extensive experimental results show that the proposed method is effective and achieves impressive results.
文章引用：麻可可, 薛丽霞, 汪荣贵, 杨娟. 多维注意力与部件关注的无监督行人重识别[J]. 计算机科学与应用, 2021, 11(5): 1301-1304. https://doi.org/10.12677/CSA.2021.115132

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