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.

中文翻译：

---

学习具有通道相关性的语义依赖以进行多标签分类

多标签图像分类是计算机视觉中一项基本且具有挑战性的任务。尽管应用 CNN-RNN 模式取得了显着的成功，但由于 RNN 模块的存在，这种方法的收敛速度很慢。本文没有使用 RNN 模块，而是提出了一种完全基于卷积神经网络 (CNN) 的新型通道相关网络，以高训练效率对标签相关性进行建模。通过创建新的注意力模块，对CNN获得的图像特征进行进一步的卷积，得到标签与通道特征图的对应关系。然后我们交替使用SE和卷积操作来消除不相关的信息，以更好地探索标签相关性。