Understanding Long Videos with Multimodal Language Models
Kanchana Ranasinghe, Xiang Li, Kumara Kahatapitiya, Michael S Ryoo
Abstract
Large Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world knowledge and strong reasoning skills of underlying LLMs influence this strong performance. Surprisingly, we discover that LLM-based approaches can yield surprisingly good accuracy on long-video tasks with limited video information, sometimes even with no video-specific information. Building on this, we explore injecting video-specific information into an LLM-based framework. We utilize off-the-shelf vision tools to extract three object-centric information modalities from videos, and then leverage natural language as a medium for fusing this information. Our resulting Multimodal Video Understanding (MVU) framework demonstrates state-of-the-art performance across multiple video understanding benchmarks. Strong performance also on robotics domain tasks establishes its strong generality. Code: github.com/kahnchana/mvu
BibTeX
@inproceedings{
ranasinghe2025understanding,
title={Understanding Long Videos with Multimodal Language Models},
author={Kanchana Ranasinghe and Xiang Li and Kumara Kahatapitiya and Michael S Ryoo},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=OxKi02I29I}
}