Rethinking Self-Training for Semi-Supervised Landmark Detection: A Selection-Free Approach-Reference-Cited by-同舟云学术

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Rethinking Self-Training for Semi-Supervised Landmark Detection: A Selection-Free Approach

Published:2024 Issue: Volume:33 Page:4952-4965
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Jin Haibo¹^ORCID,Che Haoxuan¹^ORCID,Chen Hao²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

2. Department of Computer Science and Engineering, the Department of Chemical and Biological Engineering, Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China

Funder

Hong Kong Innovation and Technology Fund

Shenzhen Science and Technology Innovation Committee Fund

Research Grants Council of Hong Kong

Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/83/10346232/10667011.pdf?arnumber=10667011

Reference87 articles.

1. Look at Boundary: A Boundary-Aware Face Alignment Algorithm

2. Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

3. Precise Facial Landmark Detection by Reference Heatmap Transformer

4. An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

5. You only Learn Once: Universal Anatomical Landmark Detection

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