Abstract
In attention mechanism research, most existing methods are hard to utilize well the information of the neural network with high computing efficiency due to heavy feature compression in the attention layer. This paper proposes a simple and general approach named Bridge Attention to address this issue. As a new idea, BA-Net straightforwardly integrates features from previous layers and effectively promotes information interchange. Only simple strategies are employed for the model implementation, similar to the SENet. Moreover, after extensively investigating the effectiveness of different previous features, we discovered a simple and exciting insight that bridging all the convolution outputs inside each block with BN can obtain better attention to enhance the performance of neural networks. BA-Net is effective, stable, and easy to use. A comprehensive evaluation of computer vision tasks demonstrates that the proposed approach achieves better performance than the existing channel attention methods regarding accuracy and computing efficiency. The source code is available at https://github.com/zhaoy376/Bridge-Attention.
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Acknowledgement
This work was partially supported by the Shenzhen Fundamental Research Program (No. JCYJ20200109142217397), Guangdong Natural Science Foundation (No. 2021A1515011794, and 2021B1515120032), Shenzhen Key Science and Technology Program (No. JSGG20210802153412036), and National Natural Science Foundation of China (No.52172350).
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Zhao, Y., Chen, J., Zhang, Z., Zhang, R. (2022). BA-Net: Bridge Attention for Deep Convolutional Neural Networks. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13681. Springer, Cham. https://doi.org/10.1007/978-3-031-19803-8_18
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