While self-attention mechanism has shown promising results for many vision tasks, it only considers the current features at a time. We show that such a manner cannot take full advantage of the attention mechanism. In this paper, we present Deep Connected Attention Network (DCANet), a novel design that boosts attention modules in a CNN model without any modification of the internal structure. To achieve this, we interconnect adjacent attention blocks, making information flow among attention blocks possible. With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning. Our DCANet is generic. It is not limited to a specific attention module or base network architecture. Experimental results on ImageNet and MS COCO benchmarks show that DCANet consistently outperforms the state-of-the-art attention modules with a minimal additional computational overhead in all test cases. All code and models are made publicly available.
@article{arxiv.2007.05099,
title = {DCANet: Learning Connected Attentions for Convolutional Neural Networks},
author = {Xu Ma and Jingda Guo and Sihai Tang and Zhinan Qiao and Qi Chen and Qing Yang and Song Fu},
journal= {arXiv preprint arXiv:2007.05099},
year = {2020}
}