20 citations · 23 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
Determined Multi-Label Learning via Similarity-Based Prompt
Meng Wei, Zhongnian Li, Peng Ying +2
In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each tra…
Learning from Reduced Labels for Long-Tailed Data
Meng Wei, Zhongnian Li, Yong Zhou +1
Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and…