1 citations · 2 across the 6 of their papers we have counts for
6 papers
Reinforced Attention Learning
Bangzheng Li, Jianmo Ni, Chen Qu +5
Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multi…
DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model
Jingkai Xu, De Cheng, Xiangqian Zhao +27
Skin diseases impose a substantial burden on global healthcare systems, driven by their high prevalence (affecting up to 70% of the population), complex diagnostic processes, and a…
Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
Yubin Wang, Xinyang Jiang, De Cheng +4
Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the…
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
Yubin Wang, Xinyang Jiang, De Cheng +3
Prompt learning has become a prevalent strategy for adapting vision-language foundation models (VLMs) such as CLIP to downstream tasks. With the emergence of large language models…
ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding
Yubin Wang, Xinyang Jiang, De Cheng +2
Video temporal grounding is an emerging topic aiming to identify specific clips within videos. In addition to pre-trained video models, contemporary methods utilize pre-trained vis…
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
Yubin Wang, Xinyang Jiang, De Cheng +2
Prompt learning has become a prevalent strategy for adapting vision-language foundation models to downstream tasks. As large language models (LLMs) have emerged, recent studies hav…