Multi-Level Label Correction by Distilling Proximate Patterns for Semi-Supervised Semantic Segmentation-Reference-Cited by-同舟云学术

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Multi-Level Label Correction by Distilling Proximate Patterns for Semi-Supervised Semantic Segmentation

Published:2024 Issue: Volume:26 Page:8077-8087
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Xiao Hui¹^ORCID,Hong Yuting¹^ORCID,Dong Li¹^ORCID,Yan Diqun¹^ORCID,Xiong Junjie²^ORCID,Zhuang Jiayan³^ORCID,Liang Dongtai¹^ORCID,Peng Chengbin¹^ORCID

Affiliation:

1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China

2. Hangzhou Shenhao Technology Co., Ltd., Hangzhou, China

3. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China

Funder

Open Foundation

Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot

Ningbo Science and Technology Innovation

National Natural Science Foundation of China

Ningbo Institute of Materials Technology & Engineering

Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10462533.pdf?arnumber=10462533

Reference60 articles.

1. Fully convolutional networks for semantic segmentation

2. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

3. Segment Anything

4. The Cityscapes Dataset for Semantic Urban Scene Understanding

5. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;Tarvainen,2017

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