5 papers
Learning from Uncertain Similarity and Unlabeled Data
Meng Wei, Zhongnian Li, Peng Ying +1
Existing similarity-based weakly supervised learning approaches often rely on precise similarity annotations between data pairs, which may inadvertently expose sensitive label info…
Seeing the Undefined: Chain-of-Action for Generative Semantic Labels
Meng Wei, Zhongnian Li, Peng Ying +1
Recent advances in vision-language models (VLMs) have demonstrated remarkable capabilities in image classification by leveraging predefined sets of labels to construct text prompts…
Learning from True-False Labels via Multi-modal Prompt Retrieving
Zhongnian Li, Jinghao Xu, Peng Ying +2
Pre-trained Vision-Language Models (VLMs) exhibit strong zero-shot classification abilities, demonstrating great potential for generating weakly supervised labels. Unfortunately, e…
ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
Zhongnian Li, Meng Wei, Peng Ying +1
Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the…
Learning from Concealed Labels
Zhongnian Li, Meng Wei, Peng Ying +2
Annotating data for sensitive labels (e.g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios. To cope with this problem, we propose a…