FDAM: full-dimension attention module for deep convolutional neural networks

The attention mechanism is an important component of cross-modal research. It can improve the performance of convolutional neural networks by distinguishing the informative parts of the feature map from the useless ones. Various kinds of attention are proposed by recent studies. Different attentions use distinct division method to weight each part of the feature map. In this paper, we propose a full-dimension attention module, which is a lightweight, fully interactive 3-D attention mechanism. FDAM generates 3-D attention maps for both spatial and channel dimensions in parallel and then multiplies them to the feature map. It is difficult to obtain discriminative attention map cell under channel interaction at a low computational cost. Therefore, we adapt a generalized Elo rating mechanism to generate cell-level attention maps. We store historical information with a slight amount of non-training parameters to spread the computation over each training iteration. The proposed module can be seamlessly integrated into the end-to-end training of the CNN framework. Experiments demonstrate that it outperforms many existing attention mechanisms on different network structures and datasets for computer vision tasks, such as image classification and object detection.