In this paper, we present novel sharp attention networks by adaptively
sampling feature maps from convolutional neural networks (CNNs) for person
re-identification (re-ID) problem. Due to the introduction of sampling-based
attention models, the proposed approach can adaptively generate sharper
attention-aware feature masks. This greatly differs from the gating-based
attention mechanism that relies soft gating functions to select the relevant
features for person re-ID. In contrast, the proposed sampling-based attention
mechanism allows us to effectively trim irrelevant features by enforcing the
resultant feature masks to focus on the most discriminative features. It can
produce sharper attentions that are more assertive in localizing subtle
features relevant to re-identifying people across cameras. For this purpose, a
differentiable Gumbel-Softmax sampler is employed to approximate the Bernoulli
sampling to train the sharp attention networks. Extensive experimental
evaluations demonstrate the superiority of this new sharp attention model for
person re-ID over the other state-of-the-art methods on three challenging
benchmarks including CUHK03, Market-1501, and DukeMTMC-reID.

本文提出了一种新的尖锐关注网络，通过用卷积神经网络（CNN）的特征图自适应地采样人员再识别（re-ID）问题，从而生成更加尖锐的关注特征屏蔽蒙版，能够更有效地筛选弱相关的特征，通过可微分的 Gumbel-Softmax 采样器进行训练，实验结果表明，这种新型尖锐关注模型在三项挑战性基准测试中优于其他最先进的方法。