Papers › Enhancing Temporal Modeling of Video LLMs via Time Gating

Enhancing Temporal Modeling of Video LLMs via Time Gating

8 Oct 2024arXiv:2410.05714archive 2025-07-28

Zi-Yuan Hu, Yiwu Zhong, Shijia Huang, Michael R. Lyu, LiWei Wang

Video Large Language Models (Video LLMs) have achieved impressive performance on video-and-language tasks, such as video question answering. However, most existing Video LLMs neglect temporal information in video data, leading to struggles with temporal-aware video understanding. To address this gap, we propose a Time Gating Video LLM (TG-Vid) designed to enhance temporal modeling through a novel Time Gating module (TG). The TG module employs a time gating mechanism on its sub-modules, comprising gating spatial attention, gating temporal attention, and gating MLP. This architecture enables our model to achieve a robust understanding of temporal information within videos. Extensive evaluation of temporal-sensitive video benchmarks (i.e., MVBench, TempCompass, and NExT-QA) demonstrates that our TG-Vid model significantly outperforms the existing Video LLMs. Further, comprehensive ablation studies validate that the performance gains are attributed to the designs of our TG module. Our code is available at https://github.com/LaVi-Lab/TG-Vid.

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Syntology Ran 6 of 9 code samples harvested from 1 repository linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 2 ran with no contract checked.

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lavi-lab/tg-vid officialmentioned in papermentioned on GitHubpytorch report

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9 samples harvested; 6 ran; 1 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · violated contract
1ran · our draft was wrong
1ran · fixture could not drive it
2ran
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