论文标题
DCANET：学习卷积神经网络的关联注意力
DCANet: Learning Connected Attentions for Convolutional Neural Networks
论文作者
论文摘要
尽管自我注意力的机制已经显示出许多视力任务的令人鼓舞的结果，但它一次仅考虑当前的功能。我们表明，这种方式无法充分利用注意机制。在本文中，我们提出了深连接的注意力网络（DCANET），这是一种新型设计，可提高CNN模型中的注意力模块，而没有任何内部结构的任何修改。为了实现这一目标，我们将相邻的注意力块互连，从而使注意力障碍之间的信息流动成为可能。使用DCANET，共同训练了CNN模型中的所有注意力块，从而提高了注意力学习的能力。我们的dcanet是通用的。它不仅限于特定的注意模块或基本网络体系结构。 ImageNet和MS Coco基准测试的实验结果表明，在所有测试用例中，Dcanet始终以最小的额外计算开销来胜过最先进的注意模块。所有代码和模型均可公开使用。
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.
