Imagine an AI that can easily grasp the key moments of an hour-long video, answering your questions without getting bogged down in irrelevant details. That's the promise of VideoTree, a new approach to video understanding that's changing how machines process lengthy footage. Traditional methods often struggle with the sheer volume of information in long videos, leading to inaccuracies and slow processing. VideoTree tackles this challenge by building a hierarchical, query-adaptive representation of the video. Think of it like creating a dynamic table of contents, focusing only on the parts relevant to your question. First, VideoTree identifies keyframes, like snapshots of important moments, and captions them. Then, it uses an LLM (Large Language Model) to assess the relevance of these captions to your query. If more detail is needed, VideoTree dives deeper, re-clustering and captioning more granular segments within the relevant sections. This allows it to extract fine-grained information efficiently, without wasting time on irrelevant parts. This innovative approach has shown impressive results on benchmarks like EgoSchema, NExT-QA, and even the challenging long-form Video-MME dataset, outperforming some existing methods, including the powerful GPT-4V, in efficiency and accuracy. The ability to quickly and accurately analyze long videos opens exciting possibilities. From analyzing human behavior in research to enhancing movie understanding for viewers, VideoTree is paving the way for a future where AI can truly comprehend the complexities of video.