Welcome to the InfoSci Platform
Reference Hub1
Research of Self-Attention in Image Segmentation

Research of Self-Attention in Image Segmentation

Fude Cao, Chunguang Zheng, Limin Huang, Aihua Wang, Jiong Zhang, Feng Zhou, Haoxue Ju, Haitao Guo, Yuxia Du
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 12
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.298619
Cite Article Cite Article

MLA

Cao, Fude, et al. "Research of Self-Attention in Image Segmentation." JITR vol.15, no.1 2022: pp.1-12. http://doi.org/10.4018/JITR.298619

APA

Cao, F., Zheng, C., Huang, L., Wang, A., Zhang, J., Zhou, F., Ju, H., Guo, H., & Du, Y. (2022). Research of Self-Attention in Image Segmentation. Journal of Information Technology Research (JITR), 15(1), 1-12. http://doi.org/10.4018/JITR.298619

Chicago

Cao, Fude, et al. "Research of Self-Attention in Image Segmentation," Journal of Information Technology Research (JITR) 15, no.1: 1-12. http://doi.org/10.4018/JITR.298619

Export Reference

Mendeley
Favorite Full-Issue Download

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 think about whether the self-attention module in this field can replace convolution operation in the future.