Enhancing Information Maximization With Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning-Reference-Cited by-同舟云学术

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Enhancing Information Maximization With Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning

Published:2024 Issue: Volume:33 Page:2058-2073
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Xu Huali¹,Liu Li²^ORCID,Zhi Shuaifeng²^ORCID,Fu Shaojing³^ORCID,Su Zhuo⁴^ORCID,Cheng Ming-Ming⁵^ORCID,Liu Yongxiang²^ORCID

Affiliation:

1. College of Computer Science, Nankai University, Tianjin, China

2. College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

3. College of Computer, National University of Defense Technology, Changsha, China

4. Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland

5. TKLNDST, College of Computer Science, Nankai University, Tianjin, China

Funder

National Key Research and Development Program of China

Academy of Finland

Infotech Project A Framework for General Spatial AI

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Key Stone Grant of the National University of Defense Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/83/10346232/10471355.pdf?arnumber=10471355

Reference69 articles.

1. Human-level concept learning through probabilistic program induction

2. Generalizing from a Few Examples

3. FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active Learning using Broad Learning System

4. A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

5. Efficient Few-Shot Object Detection via Knowledge Inheritance

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