Summary of Cart: Compositional Auto-regressive Transformer For Image Generation, by Siddharth Roheda
CART: Compositional Auto-Regressive Transformer for Image Generation
by Siddharth Roheda
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our paper introduces a novel approach to image synthesis using Auto-Regressive (AR) modeling, which leverages next-detail prediction strategy for enhanced fidelity and scalability. Unlike language models, vision tasks require addressing spatial dependencies in images. We propose iteratively adding finer details to an image compositionally, constructing it as a hierarchical combination of base and detail image factors. Our method outperforms conventional approaches and surpasses state-of-the-art methods on next-scale prediction. A key advantage is its scalability to higher resolutions without retraining the full model, making it suitable for high-resolution image generation. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create new images just like a painter! Our team developed a new way to make these images using special computer models called Auto-Regressive (AR) models. Unlike language models that can understand words, vision models need to handle the spatial relationships between pixels in an image. We came up with a clever solution by adding finer details to an image step-by-step, building it layer by layer. This approach is better than existing methods and allows us to create high-resolution images without starting from scratch. |
Keywords
* Artificial intelligence * Image generation * Image synthesis