4 papers
Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation
Ridong Han, Yawen Shen, Zhongnian Li +3
Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution s…
MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning
Zhaolong Wang, Tongfeng Sun, Mingzheng Du +1
Vision-language pre-trained models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, and prompt learning has emerged as an efficient alternative to full fi…
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…
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…