# DCANet: Learning Connected Attentions for Convolutional Neural Networks Source: [DCANet: Learning Connected Attentions for Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:24:01.852Z Authors: Xu Ma, Jingda Guo, Sihai Tang, Zhinan Qiao, Qi Chen, Qing Yang, Song Fu Published: 2020-07-09T22:37:25.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()