Loading Now

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)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
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