  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.
