An Overview of the Attention Mechanisms in Computer Vision
Xiao Yang
- 01 Dec 2020
- Vol. 1693, Iss: 1, pp 012173
About: The article was published on 01 Dec 2020. and is currently open access.
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TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Non-local Neural Networks
Xiaolong Wang,Ross Girshick,Abhinav Gupta,Kaiming He +3 more
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TL;DR: In this article, the non-local operation computes the response at a position as a weighted sum of the features at all positions, which can be used to capture long-range dependencies.
A model of saliency-based visual attention for rapid scene analysis
TL;DR: In this article, a visual attention system inspired by the behavior and the neuronal architecture of the early primate visual system is presented, where multiscale image features are combined into a single topographical saliency map.
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CBAM: Convolutional Block Attention Module
TL;DR: The proposed Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks, can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs.
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A model of saliency-based visual attention for rapid scene analysis
Laurent Itti
- 01 Jan 1998
TL;DR: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented, which breaks down the complex problem of scene understanding by rapidly selecting conspicuous locations to be analyzed in detail.
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