CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Author:
Affiliation:
1. University of Southern Mississippi,USA
2. IHPC and CFAR, Agency for Science, Technology and Research,Singapore
3. University of South Carolina,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377644.pdf?arnumber=10377644
Reference47 articles.
1. G2netpl: Generic game-theoretic network for partial-label image classification;Abdelfattah
2. PLMCL: Partial-Label Momentum Curriculum Learning for Multi-label Image Classification
3. Asymmetric loss for multi-label classification;Ben-Baruch,2020
4. Multi-label learning with incomplete class assignments
5. A simple framework for contrastive learning of visual representations;Chen
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