8 citations · 8 across the 2 of their papers we have counts for
5 papers
ExpVid: A Benchmark for Experiment Video Understanding & Reasoning
Yicheng Xu, Yue Wu, Jiashuo Yu +9
Multimodal Large Language Models (MLLMs) hold promise for accelerating scientific discovery by interpreting complex experimental procedures. However, their true capabilities are po…
VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Ziang Yan, Xinhao Li, Yinan He +6
Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-level perception and understanding. Existing methods mainly leverage LLM reasoning to…
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Jinguo Zhu, Weiyun Wang, Zhe Chen +48
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model…
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
Xinhao Li, Ziang Yan, Desen Meng +7
Reinforcement Learning (RL) benefits Large Language Models (LLMs) for complex reasoning. Inspired by this, we explore integrating spatio-temporal specific rewards into Multimodal L…
DiffVSR: Revealing an Effective Recipe for Taming Robust Video Super-Resolution Against Complex Degradations
Xiaohui Li, Yihao Liu, Shuo Cao +6
Diffusion models have demonstrated exceptional capabilities in image restoration, yet their application to video super-resolution (VSR) faces significant challenges in balancing fi…