5 papers
Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement Learning
Hao Yu, Shuning Jia, Guanghao Li +2
Vision-language models (VLMs) often struggle with geometric reasoning due to their limited perception of fundamental diagram elements. To tackle this challenge, we introduce GeoPer…
SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
Guanghao Li, Wenhao Jiang, Mingfeng Chen +6
Chain of Thought (CoT) prompting improves the reasoning performance of large language models (LLMs) by encouraging step by step thinking. However, CoT-based methods depend on inter…
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation
Guanghao Li, Mingzhi Chen, Hao Yu +4
Deep learning-based denoising models have been widely employed in vision tasks, functioning as filters to eliminate noise while retaining crucial semantic information. Additionally…
FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO
Fufangchen Zhao, Songbai Tan, Xuerui Qiu +7
Existing video large language models (VLLMs) primarily leverage prompt agnostic visual encoders, which extract untargeted facial representations without awareness of the queried in…
Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
Guanghao Li, Wenhao Jiang, Li Shen +2
Resource limitations often constrain the parameter counts of Large Language Models (LLMs), hindering their performance. While existing methods employ parameter sharing to reuse the…