12 citations · 14 across the 10 of their papers we have counts for
10 papers
Joint Semantic Token Selection and Prompt Optimization for Interpretable Prompt Learning
Yating Wang, Yaqi Zhao, Yongshun Gong +2
Vision-language models such as CLIP achieve strong visual-textual alignment, but often suffer from overfitting and limited interpretability when adapted through continuous prompt l…
Distributed Multi-Layer Editing for Rule-Level Knowledge in Large Language Models
Yating Wang, Wenting Zhao, Yaqi Zhao +3
Large language models store not only isolated facts but also rules that support reasoning across symbolic expressions, natural language explanations, and concrete instances. Yet mo…
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
Yaqi Zhao, Haoliang Sun, Yating Wang +2
Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks. However, when only par…
EFF-Grasp: Energy-Field Flow Matching for Physics-Aware Dexterous Grasp Generation
Yukun Zhao, Zichen Zhong, Yongshun Gong +2
Denoising generative models have recently become the dominant paradigm for dexterous grasp generation, owing to their ability to model complex grasp distributions from large-scale…
From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space
Lehui Li, Yuyao Wang, Jisheng Yan +5
Incorporating textual information into time-series forecasting holds promise for addressing event-driven non-stationarity; however, a fundamental modality gap hinders effective fus…
Riemannian MeanFlow for One-Step Generation on Manifolds
Zichen Zhong, Haoliang Sun, Yukun Zhao +2
Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically integrating a probability-flow ODE.…