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cs.CV2025
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach
Chen-Chen Zong, Sheng-Jun Huang
Active learning (AL), which iteratively queries the most informative examples from a large pool of unlabeled candidates for model training, faces significant challenges in the pres…
cs.CV2024
Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
Heng-Bo Fan, Ming-Kun Xie, Jia-Hao Xiao +1
Due to the lack of extensive precisely-annotated multi-label data in real word, semi-supervised multi-label learning (SSMLL) has gradually gained attention. Abundant knowledge embe…