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20242026
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cs.CV2026

Conditional Multi-Event Temporal Grounding in Long-Form Video

Yuanhao Zou, Arthad Kulkarni, Lucas Tonanez +12

Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing every event that satisfies compositi…

cs.CV2026

STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models

Yiming Liang, Yixiao Chen, Yiyang Zhou +8

Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-language models (LVLMs) often a…

cs.CV2026

VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing

Andong Deng, Dawei Du, Zhenfang Chen +7

Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. Whi…

cs.CV2025

SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models

Andong Deng, Taojiannan Yang, Shoubin Yu +5

Large Multimodal Models (LMMs) have achieved remarkable progress across various capabilities; however, complex video reasoning in the scientific domain remains a significant and ch…

cs.CV2025

A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering

Yuanhao Zou, Shengji Jin, Andong Deng +3

Effectively applying Vision-Language Models (VLMs) to Video Question Answering (VideoQA) hinges on selecting a concise yet comprehensive set of frames, as processing entire videos…

cs.CV2024

Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level

Andong Deng, Tongjia Chen, Shoubin Yu +6

In this paper, we introduce Motion-Grounded Video Reasoning, a new motion understanding task that requires generating visual answers (video segmentation masks) according to the inp…