Summary of Everything Is a Video: Unifying Modalities Through Next-frame Prediction, by G. Thomas Hudson et al.
Everything is a Video: Unifying Modalities through Next-Frame Prediction
by G. Thomas Hudson, Dean Slack, Thomas Winterbottom, Jamie Sterling, Chenghao Xiao, Junjie Shentu, Noura Al Moubayed
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 This paper introduces a novel framework for multimodal learning, which integrates information from various modalities such as text, images, audio, and video. The proposed approach, task reformulation, extends beyond natural language processing (NLP) to enable a single model to handle different modalities without modality-specific components. This method treats all inputs and outputs as sequential frames in a video, allowing for seamless integration of modalities and effective knowledge transfer across tasks. The framework is evaluated on various tasks, including text-to-text, image-to-text, video-to-video, video-to-text, and audio-to-text, demonstrating the model’s ability to generalize across modalities with minimal adaptation. The authors show that task reformulation can simplify multimodal model design across various tasks, laying the groundwork for more generalized multimodal foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to teach a computer to understand and work with different types of information, like text, images, audio, and video. This paper shows how to make computers do just that by creating a new way to learn from multiple sources. The idea is called “task reformulation” and it lets computers handle different types of information without needing special tools for each type. The approach is tested on many different tasks, such as translating text or describing images, and the results show that it works well. This means that in the future, we could have more powerful computers that can learn from a wide range of sources. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
