论文标题
DAF-NET：一种基于显着性的弱监督方法的双重注意融合方法，用于细颗粒图像分类
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification
论文作者
论文摘要
细粒度的图像分类是一个具有挑战性的问题，因为难以找到判别特征。基本上，要处理这种情况，还有两种方法要走。一种是使用基于注意力的方法专注于信息丰富的领域，而另一个旨在在功能之间找到高阶。此外，对于基于注意力的方法，有两个方向，基于激活和基于检测的方向，这些方向被学者证明是有效的。但是，罕见的工作着重于将两种类型的注意力与高阶功能融合在一起。在本文中，我们提出了一种新颖的DAF方法，该方法融合了两种类型的注意力，并将它们用作深度双线性转换模块中的PAF（部分注意过滤器），以挖掘物体的各个部分之间的关系。简而言之，我们的网络由学生网构建，他们试图输出两个注意力图，而教师网则使用这两个地图作为经验信息来完善结果。实验结果表明，只有学生网可以在CUB数据集中获得87.6％的精度，而与教师网进行合作可以达到89.1％的精度。
Fine-grained image classification is a challenging problem, since the difficulty of finding discriminative features. To handle this circumstance, basically, there are two ways to go. One is use attention based method to focus on informative areas, while the other one aims to find high order between features. Further, for attention based method there are two directions, activation based and detection based, which are proved effective by scholars. However ,rare work focus on fusing two types of attention with high order feature. In this paper, we propose a novel DAF method which fuse two types of attention and use them to as PAF(part attention filter) in deep bilinear transformation module to mine the relationship between separate parts of an object. Briefly, our network constructed by a student net who attempt to output two attention maps and a teacher net uses these two maps as empirical information to refine the result. The experiment result shows that only student net could get 87.6% accuracy in CUB dataset while cooperating with teacher net could achieve 89.1% accuracy.
