Understanding Long Videos with Multimodal Language Models
Kanchana Ranasinghe Xiang Li Kumara Kahatapitiya Michael S Ryoo
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
long-video, visual question answering, interpretability
Investigates effects of LLM strengths on Long Video QnA tasks. Introduces Multimodal Video Understanding (MVU) framework that incorporates object-centric data from pre-trained models and sets a new state-of-the-art in long-video tasks.