4 papers
Perceptual Flow Network for Visually Grounded Reasoning
Yangfu Li, Yuning Gong, Hongjian Zhan +8
Despite the success of Large-Vision Language Models (LVLMs), general optimization objectives (e.g., standard MLE) fail to constrain visual trajectories, leading to language bias an…
DeepScan: A Training-Free Framework for Visually Grounded Reasoning in Large Vision-Language Models
Yangfu Li, Hongjian Zhan, Jiawei Chen +3
Humans can robustly localize visual evidence and provide grounded answers even in noisy environments by identifying critical cues and then relating them to the full context in a bo…
OIG-Bench: A Multi-Agent Annotated Benchmark for Multimodal One-Image Guides Understanding
Jiancong Xie, Wenjin Wang, Zhuomeng Zhang +5
Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities. However, evaluating their capacity for human-like understanding in One-Image…
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models
Zekai Zhao, Qi Liu, Kun Zhou +4
Despite the remarkable reasoning performance, eliciting the long chain-of-thought (CoT) ability in large language models (LLMs) typically requires costly reinforcement learning or…