2 citations · 4 across the 10 of their papers we have counts for
7 papers · 1 filter
Unleashing Perception-Time Scaling to Multimodal Reasoning Models
Yifan Li, Zhenghao Chen, Ziheng Wu +7
Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of L…
VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories?
Jiachen Yu, Yufei Zhan, Ziheng Wu +3
Recent extensive works have demonstrated that by introducing long CoT, the capabilities of MLLMs to solve complex problems can be effectively enhanced. However, the reasons for the…
Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
Zhentao He, Can Zhang, Ziheng Wu +6
Recent advancements in multimodal large language models have enhanced document understanding by integrating textual and visual information. However, existing models exhibit incompl…
FOCUS: Unified Vision-Language Modeling for Interactive Editing Driven by Referential Segmentation
Fan Yang, Yousong Zhu, Xin Li +6
Recent Large Vision Language Models (LVLMs) demonstrate promising capabilities in unifying visual understanding and generative modeling, enabling both accurate content understandin…
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Yufei Zhan, Ziheng Wu, Yousong Zhu +10
Despite notable advancements in multimodal reasoning, leading Multimodal Large Language Models (MLLMs) still underperform on vision-centric multimodal reasoning tasks in general sc…
Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models
Yufei Zhan, Hongyin Zhao, Yousong Zhu +4
Large Multimodal Models (LMMs) have recently demonstrated remarkable visual understanding performance on both vision-language and vision-centric tasks. However, they often fall sho…
Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning
Yufei Zhan, Yousong Zhu, Shurong Zheng +4
Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning. Recently, preference optimization, derived from the lang…