Learning semantic dependencies with channel correlation for multi-label classification

Multi-label image classification is a fundamental and challenging task in computer vision. Although remarkable success has been achieved by applying CNN-RNN pattern, such method has a slow convergence rate due to the existence of RNN module. Instead of utilizing the RNN modules, this paper proposes a novel channel correlation network which is fully based on convolutional neural network (CNN) to model the label correlations with high training efficiency. By creating a new attention module, the image features obtained by CNN are further convoluted to obtain the correspondence between the label and the channel-wise feature map. Then we use the SE and the convolution operation alternately to eliminate the irrelevant information to better explore the label correlation. Experiments on PASCAL VOC 2007 and MIRFlickr25k show that our model can effectively exploit the dependencies between multiple tags to achieve better performance.