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14 March 2022 Self-attention technology in image segmentation
Fude Cao, Xueyun Lu
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Proceedings Volume 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021); 1216511 (2022) https://doi.org/10.1117/12.2628135
Event: 2021 International Conference on Intelligent Traffic Systems and Smart City, 2021, Zhengzhou, China
Abstract
Although the traditional Convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past two years. And we think about whether the self-attention module in this field can replace convolution operation in the future. The answer to this review is yes, so it is recommended that the focus of future research be on the self-attention module
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Fude Cao and Xueyun Lu "Self-attention technology in image segmentation", Proc. SPIE 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 1216511 (14 March 2022); https://doi.org/10.1117/12.2628135
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KEYWORDS
Image segmentation
Convolution
Expectation maximization algorithms
Neural networks
Computer vision technology
Convolutional neural networks
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