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 establish its strong generality. Code:this https URL
View on arXiv@article{ranasinghe2025_2403.16998,
title={ Understanding Long Videos with Multimodal Language Models },
author={ Kanchana Ranasinghe and Xiang Li and Kumara Kahatapitiya and Michael S. Ryoo },
journal={arXiv preprint arXiv:2403.16998},
year={ 2025 }
}