Summary of 4m-21: An Any-to-any Vision Model For Tens Of Tasks and Modalities, by Roman Bachmann et al.
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
by Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, Amir Zamir
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 expands the capabilities of multimodal and multitask foundation models, such as 4M or UnifiedIO. Current models show promising results but are limited by the number of modalities and tasks they were trained on. The authors train a single model on tens of diverse modalities, including semantic and geometric ones, feature maps from recent state-of-the-art models like DINOv2 and ImageBind, pseudo labels from specialist models like SAM and 4DHumans, and novel modalities for interaction and steering generation. The paper also performs discrete tokenization on various modalities to enable multimodal generation capabilities. The authors demonstrate the possibility of training one model to solve at least 3x more tasks/modalities than existing ones without a loss in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at understanding and working with different types of data, like pictures, words, and sounds. Right now, these “multimodal” models can only do a few things, but the authors found a way to train one model to do many more things without getting worse at what it already does. They did this by teaching the model lots of different ways to understand and work with data. This is important because it could help us make computers that are even better at understanding and responding to us. |
Keywords
» Artificial intelligence » Sam » Tokenization
