VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
Yue Fan Xiaojian Ma Rujie Wu Yuntao Du Jiaqi Li Zhi Gao Qing Li
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.