1 citations · 2 across the 19 of their papers we have counts for
17 papers · 1 filter
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…
TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs
Yuhan Zhu, Changlian Ma, Xiangyu Zeng +12
Video multimodal large language models (MLLMs) can describe what happens in a video, but rarely identify when the supporting evidence occurs. We study generalist video temporal gro…
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…
Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction
Tianxiang Jiang, Linquan Wu, Sheng Xia +5
Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in…
ViCuR: Visual Cues as Recoverable Privilege for Multimodal On-Policy Distillation
Kanghui Tian, Siyuan Liu, Ziang Yan +3
On-policy distillation (OPD) improves reasoning by training a student on trajectories sampled from its own policy under supervision from a teacher. In multimodal reasoning, a commo…
Video-o3: Native Interleaved Clue Seeking for Long Video Multi-Hop Reasoning
Xiangyu Zeng, Zhiqiu Zhang, Yuhan Zhu +12
Existing multimodal large language models for long-video understanding predominantly rely on uniform sampling and single-turn inference, limiting their ability to identify sparse y…
InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision
Chenting Wang, Yuhan Zhu, Yicheng Xu +6
Large-scale video-text pretraining achieves strong performance but depends on noisy, synthetic captions with limited semantic coverage, often overlooking implicit world knowledge s…
VideoTG-R1: Boosting Video Temporal Grounding via Curriculum Reinforcement Learning on Reflected Boundary Annotations
Lu Dong, Haiyu Zhang, Han Lin +8
Video temporal grounding (VTG) aims to locate precise segments in videos based on language queries, which is a fundamental challenge in video understanding. While recent Multimodal…
DSI-Bench: A Benchmark for Dynamic Spatial Intelligence
Ziang Zhang, Zehan Wang, Guanghao Zhang +5
Reasoning about dynamic spatial relationships is essential, as both observers and objects often move simultaneously. Although vision-language models (VLMs) and visual expertise mod…
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…
StreamForest: Efficient Online Video Understanding with Persistent Event Memory
Xiangyu Zeng, Kefan Qiu, Qingyu Zhang +9
Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in video understanding. However, their effectiveness in real-time streaming scenarios remains li…
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…
FreeRet: MLLMs as Training-Free Retrievers
Yuhan Zhu, Xiangyu Zeng, Chenting Wang +6
Multimodal large language models (MLLMs) are emerging as versatile foundations for mixed-modality retrieval. Yet, they often require heavy post-hoc training to convert them into co…
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…
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…
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…
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning
Xiangyu Zeng, Kunchang Li, Chenting Wang +10
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in short video understanding. However, understanding long-form videos still remains challenging fo…