Summary of Wolf: Captioning Everything with a World Summarization Framework, by Boyi Li and Ligeng Zhu and Ran Tian and Shuhan Tan and Yuxiao Chen and Yao Lu and Yin Cui and Sushant Veer and Max Ehrlich and Jonah Philion and Xinshuo Weng and Fuzhao Xue and Andrew Tao and Ming-yu Liu and Sanja Fidler and Boris Ivanovic and Trevor Darrell and Jitendra Malik and Song Han and Marco Pavone
Wolf: Captioning Everything with a World Summarization Framework
by Boyi Li, Ligeng Zhu, Ran Tian, Shuhan Tan, Yuxiao Chen, Yao Lu, Yin Cui, Sushant Veer, Max Ehrlich, Jonah Philion, Xinshuo Weng, Fuzhao Xue, Andrew Tao, Ming-Yu Liu, Sanja Fidler, Boris Ivanovic, Trevor Darrell, Jitendra Malik, Song Han, Marco Pavone
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed Wolf framework is an automated video captioning system that combines the strengths of Vision Language Models (VLMs) to accurately summarize videos. By utilizing both image and video models, Wolf captures different levels of information and summarizes them efficiently, enabling applications such as enhanced video understanding, auto-labeling, and captioning. To evaluate caption quality, a new metric called CapScore is introduced, assessing the similarity and quality of generated captions compared to ground truth captions. The framework is tested on four human-annotated datasets in three domains: autonomous driving, general scenes, and robotics. Results show that Wolf outperforms state-of-the-art approaches and commercial solutions, such as GPT-4V, achieving a 55.6% improvement in CapScore quality and a 77.4% improvement in similarity on challenging driving videos. The paper also establishes a benchmark for video captioning and introduces a leaderboard to accelerate advancements in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wolf is a new approach to video captioning that uses a combination of different machine learning models to understand what’s happening in a video. It can be used to automatically add captions to videos, which is useful for people who are deaf or hard of hearing, or for anyone who wants to quickly get the main points of a video. The system is tested on four different sets of videos and is shown to be more accurate than other approaches that are currently available. |
Keywords
* Artificial intelligence * Gpt * Machine learning
