Double Attention for Multi-Label Image Classification
Haiying Zhao, Wei Zhou, Xiaogang Hou, Hui Zhu
Abstract
Multi-label image classification is an essential task in image processing. How to improve the correlation between labels by learning multi-scale features from images is a very challenging problem. We propose a Double Attention Network (DAN) to improve the correlation between image feature regions and labels, as well as between labels and labels. Firstly, the dynamic learning strategy is used to extract the multi-scale features of the image to solve the problem of inconsistent scale of objects in the image. Secondly, in order to improve the correlation between the image feature regions and the labels, we use the spatial attention module to focus on the important regions of the image to learn their salient features, while we use the channel attention module to model the correlation between the channels to improve the correlation between the labels. Finally, the output features of two attention modules are fused as one multi-label image classification model. Experiments on MS-COCO 2014 dataset, Pascal VOC 2007 dataset and NUS-WIDE dataset demonstrate that our model is significantly better than the state-of-the-art models. Besides, visualization analyses show that our model has a strong ability for image salient feature learning and label correlation capturing.