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20232026
most citedInternVideo2: Scaling Foundation Models for Multimodal Video Understanding

16 citations · 25 across the 26 of their papers we have counts for

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14 papers · 1 filter

cs.CV2025

TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs

Jun Zhang, Teng Wang, Yuying Ge +4

This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in…

cs.LG2025

GSPN-2: Efficient Parallel Sequence Modeling

Hongjun Wang, Yitong Jiang, Collin McCarthy +12

Efficient vision transformer remains a bottleneck for high-resolution images and long-video related real-world applications. Generalized Spatial Propagation Network (GSPN) addresse…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models

Weihang Wang, Xinhao Li, Ziyue Wang +5

Object hallucination in Large Vision-Language Models (LVLMs) significantly impedes their real-world applicability. As the primary component for accurately interpreting visual infor…

cs.CV2025

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…