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VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding

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arxiv 2403.11481 v2 pith:T3TF6VUG submitted 2024-03-18 cs.CV

Yue Fan , Xiaojian Ma , Rujie Wu , Yuntao Du , Jiaqi Li , Zhi Gao , Qing Li This is my paper
classification cs.CV
keywords modelsvideomemoryunderstandingvideoagentagentfoundationincluding
verification ladder T0 review T1 audit T2 compute T3 formal
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We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos. In particular, the proposed multimodal agent VideoAgent: 1) constructs a structured memory to store both the generic temporal event descriptions and object-centric tracking states of the video; 2) given an input task query, it employs tools including video segment localization and object memory querying along with other visual foundation models to interactively solve the task, utilizing the zero-shot tool-use ability of LLMs. VideoAgent demonstrates impressive performances on several long-horizon video understanding benchmarks, an average increase of 6.6% on NExT-QA and 26.0% on EgoSchema over baselines, closing the gap between open-sourced models and private counterparts including Gemini 1.5 Pro.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  4. Augmented Vision-Language Models: A Systematic Review

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