Few-shot semantic segmentation: a review on recent approaches-Reference-Cited by-同舟云学术

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Few-shot semantic segmentation: a review on recent approaches

Published:2023-07-14 Issue:25 Volume:35 Page:18251-18275
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Chang Zhaobin,Lu Yonggang,Ran Xingcheng,Gao Xiong,Wang Xiangwen

Funder

National Key Research and Development Program of China

Higher Education Innovation Fund Project of Gansu Province

Natural Science Fund of Gansu Province

Youth Science and Technology Fund Project of Gansu Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08758-9.pdf

Reference106 articles.

1. Wang W, Zhou T, Yu F, Dai J, Konukoglu E, Van Gool L (2021) Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 7303–7313

2. Zhou T, Wang W, Konukoglu E, Van Gool L (2022) Rethinking semantic segmentation: A prototype view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2582–2593

3. Yuan X, Shi J, Gu L (2021) A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst Appl 169:114417. https://doi.org/10.1016/j.eswa.2020.114417

4. Asgari Taghanaki S, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G (2021) Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev 54(1):137–178

5. Feng D, Haase-Schütz C, Rosenbaum L, Hertlein H, Glaeser C, Timm F, Wiesbeck W, Dietmayer K (2020) Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Trans Intell Transp Syst 22(3):1341–1360

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