Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance 事件

Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance arXiv:2606.07630v1 Announce Type: cross Abstract: Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly on minority classes. Among existing solutions, active learning offers an effective and efficient paradigm by selectively querying the most informative and balanced samples for annot