most citedMini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CV2025

Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models

Gen Luo, Wenhan Dou, Wenhao Li +9

This paper focuses on monolithic Multimodal Large Language Models (MLLMs), which integrate visual encoding and language decoding into a single model. Existing structures and pre-tr…

cs.CV2025

Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces

Gen Luo, Ganlin Yang, Ziyang Gong +15

The remarkable progress of Multimodal Large Language Models (MLLMs) has attracted increasing attention to extend them to physical entities like legged robot. This typically require…

cs.AI2025

ZeroGUI: Automating Online GUI Learning at Zero Human Cost

Chenyu Yang, Shiqian Su, Shi Liu +11

The rapid advancement of large Vision-Language Models (VLMs) has propelled the development of pure-vision-based GUI Agents, capable of perceiving and operating Graphical User Inter…

cs.CV2025

Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings

Xingguang Wei, Haomin Wang, Shenglong Ye +7

We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable things and the semantic regions of uncountable stuff in computer-ai…

cs.CV2025

VisualPRM: An Effective Process Reward Model for Multimodal Reasoning

Weiyun Wang, Zhangwei Gao, Lianjie Chen +12

We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (M…

cs.CV2025

Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding

Zhaokai Wang, Xizhou Zhu, Xue Yang +8

Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the…