93 citations · 427 across the 35 of their papers we have counts for
38 papers
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24
Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current…
InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning
Ziang Yan, Sheng Xia, Jiashuo Yu +10
Recent progress in foundation models has shifted toward agentic behavior involving multi-step reasoning and tool use. However, open-source efforts largely focus on text-dominant se…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
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…
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Weiyun Wang, Zhangwei Gao, Lixin Gu +72
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…
ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
Hongbo Liu, Jingwen He, Yi Jin +11
Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrat…
InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Models
Nianchen Deng, Lixin Gu, Shenglong Ye +17
Recent benchmarks and datasets have been proposed to improve spatial reasoning in vision-language models (VLMs), yet existing open resources remain limited in scale, visual diversi…
VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos
Jiashuo Yu, Yue Wu, Meng Chu +14
We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations…
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…
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
Dian Zheng, Ziqi Huang, Hongbo Liu +9
Video generation has advanced significantly, evolving from producing unrealistic outputs to generating videos that appear visually convincing and temporally coherent. To evaluate t…
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
Weichen Fan, Chenyang Si, Junhao Song +16
We present Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation. The overall Vchitect-2.0 system h…
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
Yi Wang, Xinhao Li, Ziang Yan +13
This paper aims to improve the performance of video multimodal large language models (MLLM) via long and rich context (LRC) modeling. As a result, we develop a new version of Inter…
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…
VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
Xinhao Li, Yi Wang, Jiashuo Yu +10
Long-context video modeling is critical for multimodal large language models (MLLMs), enabling them to process movies, online video streams, and so on. Despite its advances, handli…
Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment
Ziang Yan, Zhilin Li, Yinan He +9
Current multimodal large language models (MLLMs) struggle with fine-grained or precise understanding of visuals although they give comprehensive perception and reasoning in a spect…
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
Qingyun Li, Zhe Chen, Weiyun Wang +37
Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resem…
VideoMamba: State Space Model for Efficient Video Understanding
Kunchang Li, Xinhao Li, Yi Wang +4
Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMa…
InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
Yi Wang, Kunchang Li, Xinhao Li +17
We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialog…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang, Yinan He, Jiashuo Yu +13
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
Kunchang Li, Yali Wang, Yinan He +9
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these…
Harvest Video Foundation Models via Efficient Post-Pretraining
Yizhuo Li, Kunchang Li, Yinan He +5
Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets. In this paper, we…