FcaNet: Frequency Channel Attention Networks

By Zequn Qin, Pengyi Zhang, Fei Wu, Xi Li. IEEE International Conference on Computer Vision, 2020

1,135 citations recorded by the source corpus.

Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms while ignoring a fundamental problem, i.e., channel attention mechanism uses scalar to represent channel, which is difficult due to massive information loss. In this work, we start from a different view and regard the channel representation problem as a compression process using frequency analysis. Based on the frequency analysis, we mathematically prove that the conventional global average pooling is a special case of the feature decomposition in the frequency domain. With the proof, we naturally generalize the compression of the channel attention mechanism in the frequency domain and propose our method with multi-spectral channel attention, termed as FcaNet. FcaNet is simple but effective. We can change a few lines of code in the calculation to implement our method within existing channel attention methods. Moreover, the proposed method achieves state-of-the-art results compared with other channel attention methods on image classification, object detection, and instance segmentation tasks. Our method could consistently outperform the baseline SENet, with the same number of parameters and the same computational cost. Our code and models are publicly available at https://github.com/cfzd/FcaNet.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1834a1ff37052895c42906ceb163d9306badc00b

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Citations in the corpus

Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition (2021, methodology): “[20] introduce frequency analysis to attention mechanism and present a new method termed FcaNet to compress information loss in scalar-based channel representations.”
Visual attention network (2022, background): “Channel adaptability is also proven important for visual tasks [33,85,59,80,10].”
Visual Attention Methods in Deep Learning: An In-Depth Survey (2022, background): “[76] argued that the global average pooling operation can be replaced with frequency components.”
SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention (2024, neutral): “Existing plug-and-play attention methods can be primarily categorized into three types: channel attention [23, 27, 62, 55, 31, 41], spatial attention [22, 56, 42, 9], and hybrid channel-spatial attention [57, 40, 38, 14, 20, 65, 39, 61].”
Attention mechanisms in computer vision: A survey (2021, background): “[57] rethought global information captured from the viewpoint of compression and analysed global average pooling in the frequency domain.”