Existing person re-identification (re-id) deep learning methods rely heavily on the utilisation of large and computationally expensive convolutional neural networks. They are therefore not scalable to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we address this limitation via jointly learning re-id attention selection. Specifically, we formulate a novel harmonious attention network (HAN) framework to jointly learn soft pixel attention and hard region attention alongside simultaneous deep feature representation learning, particularly enabling more discriminative re-id matching by efficient networks with more scalable model inference and feature matching. Extensive evaluations validate the cost-effectiveness superiority of the proposed HAN approach for person re-id against a wide variety of state-of-the-art methods on four large benchmark datasets: CUHK03, Market-1501, DukeMTMC, and MSMT17.

中文翻译：

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基于和谐注意力的可扩展人重识别

现有的人员重新识别 (re-id) 深度学习方法严重依赖于大型且计算成本高的卷积神经网络的使用。由于推理过程冗长，计算成本高，因此它们无法扩展到需要处理大量监控视频数据的大规模重新识别部署场景。在这项工作中，我们通过联合学习重新识别注意力选择来解决这个限制。具体来说，我们制定了一个新的和谐注意网络 (HAN) 框架来联合学习软像素注意和硬区域注意以及同步深度特征表示学习，特别是通过具有更可扩展模型推理和特征匹配的高效网络实现更具辨别力的重新识别匹配。