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20242026
most citedInternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling

1 citations · 1 across the 19 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

VideoJev: System One Model for Video Understanding and Interaction

Yuhan Zhu, Xiangyu Zeng, Qingyi Si +15

Visual agents must turn video observations into timely decisions to track progress and choose what to do next. Building on the emerging System One paradigm, we introduce VideoJev,…

cs.CV2026

PercepCap: Video Captioner with Structured Spatio-Temporal Perception

Yifan Xu, Zihao Wang, Zhixiao Wang +6

Video captioning requires fine-grained spatio-temporal understanding of videos, including spatial perception of where objects are located and temporal perception of when events occ…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance

Yunfeng Liu, Yuandong Yang, Jiarui Han +5

Visually impaired individuals (VIIs) encounter significant daily challenges due to limited access to visual information. Although Multimodal Large Language Models (MLLMs) have achi…

cs.CV2026

StreamOV: Streaming Omni-Video Understanding via Evidence-Guided Memory and Response Triggering

Ming Xie, Zizheng Huang, Xudong Tan +6

While streaming omni-video understanding demands continuous perception and proactive, real-time interaction, this crucial area remains largely under-explored. Current omni-modal me…

cs.CV2026

USV: Towards Understanding the User-generated Short-form Videos

Haoyue Cheng, Su Xu, Liwei Jin +3

Several large-scale video datasets have been published these years and have advanced the area of video understanding. However, the newly emerged user-generated short-form videos ha…

cs.CV2026

RIVER: A Real-Time Interaction Benchmark for Video LLMs

Yansong Shi, Qingsong Zhao, Tianxiang Jiang +3

The rapid advancement of multimodal large language models has demonstrated impressive capabilities, yet nearly all operate in an offline paradigm, hindering real-time interactivity…

cs.CV2026

LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization

Zhenpeng Huang, Jiaqi Li, Zihan Jia +6

We present LongVPO, a novel two-stage Direct Preference Optimization framework that enables short-context vision-language models to robustly understand ultra-long videos without an…

cs.CV2026

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…