4 citations · 13 across the 18 of their papers we have counts for
5 papers · 1 filter
Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning
Zejun Li, Yingxiu Zhao, Jiwen Zhang +6
Current visual reasoning methods mainly focus on exploring specific reasoning modes. Although improvements can be achieved in particular domains, they struggle to develop general r…
Simple o3: Towards Interleaved Vision-Language Reasoning
Ye Wang, Qianglong Chen, Zejun Li +4
Multimodal Large Language Models (MLLMs) have shown impressive performance on vision-language tasks, but their long Chain-of-Thought (CoT) capabilities in multimodal scenarios rema…
MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models
Dianyi Wang, Siyuan Wang, Zejun Li +6
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across multi-modal tasks by scaling model size and training data. However, these dense LVLMs incur sig…
AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs
Xuanwen Ding, Chengjun Pan, Zejun Li +3
Evaluating multimodal large language models (MLLMs) is increasingly expensive, as the growing size and cross-modality complexity of benchmarks demand significant scoring efforts. T…
OViP: Online Vision-Language Preference Learning for VLM Hallucination
Shujun Liu, Siyuan Wang, Zejun Li +3
Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs. Although recent training-based approaches aim to mi…