Few-shot segmentation based on multi-level and cross-scale clustering-Reference-Cited by-同舟云学术

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Few-shot segmentation based on multi-level and cross-scale clustering

Published:2024-02-29 Issue:1 Volume:36 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Yuan Shuai¹,Qiu Junhai²,Xu Hongxia¹,Zhang Yan¹³,Zhang Jiaxing³

Affiliation:

1. Information Engineering College, Yantai Institute of Technology, Yantai, People’s Republic of China

2. Department of Electrical Science & New Energy Engineering, Yantai Engineering & Technology College, Yantai, People’s Republic of China

3. School of Information Science and Technology, Ludong University, Yantai, People’s Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2023.2287972

Reference50 articles.

1. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2. FCM: The fuzzy c-means clustering algorithm

3. Boudiaf, M., Kervadec, H., Masud Z. I., Piantanida, P., Ayed, I.B., & Dolz, J. (2021). Few-shot segmentation without meta-learning: A good transductive inference is all you need?. In Michael S. Brown (Ed.), Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13979–13988). IEEE.

4. A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective

5. ImageNet: A large-scale hierarchical image database

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