Deep Representation Learning With Part Loss for Person Re-Identification
Deep Representation Learning With Part Loss for Person Re-Identification
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DOI：
10.1109/tip.2019.2891888
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发表时间：
2017-07
影响因子：
10.6
通讯作者：
Hantao Yao;Shiliang Zhang;Richang Hong;Yongdong Zhang;Changsheng Xu;Q. Tian
中科院分区：
文献类型：
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作者：
Hantao Yao;Shiliang Zhang;Richang Hong;Yongdong Zhang;Changsheng Xu;Q. Tian
学习未见过的人图像的区分表示对于人重新识别（ReID）至关重要。目前的大多数方法在分类任务中学习深度表示，这基本上最大限度地减少了训练集上的经验分类风险。正如我们的实验所示，这种表示很容易在训练集上的有区别的人体部位上过度拟合。为了获得对看不见的人图像的区分能力，我们提出了一种名为部分损失网络的深度表示学习过程，以最大限度地降低训练人图像的经验分类风险和看不见的人图像的表示学习风险。通过建议的部分损失来评估表示学习风险，该部分损失自动检测人体部位并分别计算每个部位上的人分类损失。与传统的全局分类损失相比，同时考虑部分损失迫使深度网络学习不同身体部位的表示，并获得对看不见的人的区分能力。在三个人ReID数据集上的实验结果，即，Market1501、CUHK03和VIPeR表明，我们的表示优于现有的深度表示。
Learning discriminative representations for unseen person images is critical for person re-identification (ReID). Most of the current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments, such representations easily get over-fitted on a discriminative human body part on the training set. To gain the discriminative power on unseen person images, we propose a deep representation learning procedure named part loss network, to minimize both the empirical classification risk on training person images and the representation learning risk on unseen person images. The representation learning risk is evaluated by the proposed part loss, which automatically detects human body parts and computes the person classification loss on each part separately. Compared with traditional global classification loss, simultaneously considering part loss enforces the deep network to learn representations for different body parts and gain the discriminative power on unseen persons. Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that our representation outperforms existing deep representations.