论文标题
单杆人重新识别的深度学习的三胞胎置换方法
Triplet Permutation Method for Deep Learning of Single-Shot Person Re-Identification
论文作者
论文摘要
通过训练深卷积神经网络来解决单杆人重新识别（RE-ID），这是一个艰巨的挑战，由于缺乏培训数据，因此每人只有两张图像。这导致模型过度拟合，导致性能退化。本文从某个重新ID数据集制定了三胞胎置换方法，以生成多个训练集。这是馈送三重态网络的新型策略，可减少单杆重新ID模型的过度拟合。在PRID2011最具挑战性的RE-ID数据集之一中证明了改进的性能，证明了该方法的有效性。
Solving Single-Shot Person Re-Identification (Re-Id) by training Deep Convolutional Neural Networks is a daunting challenge, due to the lack of training data, since only two images per person are available. This causes the overfitting of the models, leading to degenerated performance. This paper formulates the Triplet Permutation method to generate multiple training sets, from a certain re-id dataset. This is a novel strategy for feeding triplet networks, which reduces the overfitting of the Single-Shot Re-Id model. The improved performance has been demonstrated over one of the most challenging Re-Id datasets, PRID2011, proving the effectiveness of the method.
