论文标题
StadB：一个自定位的注意力指导的人重新识别的ADB网络
STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification
论文作者
论文摘要
最近，通过功能擦除，批量Dropblock网络（BDB）证明了其对人图像表示和重新识别任务的有效性。但是，BDB删除了可能导致亚最佳结果的功能\ textbf {随机}。在本文中，我们提出了一种新颖的自节庇护注意力引导的自适应下降网络（StadB），用于\ textbf {appaptimentifice}删除最歧视的区域。具体而言，StadB首先通过频道池获得了注意力图，并通过阈值注意注意力图返回掉落面具。然后，将输入功能和自定位注意力引导的滴面罩乘以生成掉落的特征图。此外，Stadb利用空间和渠道注意力来学习更好的功能图，并迭代地训练Person Re-ID的功能掉落模块。在几个基准数据集上的实验表明，所提出的StadB的表现优于人重新ID的许多其他相关方法。本文的源代码发布于：\ textColor {red} {\ url {https://github.com/wangxiao5791509/stadb_reid}}。
Recently, Batch DropBlock network (BDB) has demonstrated its effectiveness on person image representation and re-identification task via feature erasing. However, BDB drops the features \textbf{randomly} which may lead to sub-optimal results. In this paper, we propose a novel Self-Thresholding attention guided Adaptive DropBlock network (STADB) for person re-ID which can \textbf{adaptively} erase the most discriminative regions. Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map. Then, the input features and self-thresholding attention guided drop mask are multiplied to generate the dropped feature maps. In addition, STADB utilizes the spatial and channel attention to learn a better feature map and iteratively trains the feature dropping module for person re-ID. Experiments on several benchmark datasets demonstrate that the proposed STADB outperforms many other related methods for person re-ID. The source code of this paper is released at: \textcolor{red}{\url{https://github.com/wangxiao5791509/STADB_ReID}}.
