Summary of Videotree: Adaptive Tree-based Video Representation For Llm Reasoning on Long Videos, by Ziyang Wang et al.
VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos
by Ziyang Wang, Shoubin Yu, Elias Stengel-Eskin, Jaehong Yoon, Feng Cheng, Gedas Bertasius, Mohit Bansal
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called VideoTree for training-free long-form video understanding. The challenge lies in extracting relevant information from high-redundancy video data. VideoTree iteratively refines keyframe selection based on query relevance, incorporating hierarchical structure to extract details at multiple granularities. This allows the model to handle varying query levels of detail efficiently. Experiments show improved accuracy and efficiency compared to existing training-free approaches on benchmarks EgoSchema, NExt-QA, and Video-MME. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a long video without knowing what to look for. This paper solves that problem by creating a special way to analyze videos without needing tons of training data. They called it VideoTree. It works by looking at the most important parts of the video and then breaking them down into smaller pieces. This helps the model understand what’s important in the video and answer questions about it more accurately. The results show that this method is better than others at understanding long videos without needing extra training. |
