Summary of A Survey on Vision Autoregressive Model, by Kai Jiang and Jiaxing Huang
A Survey on Vision Autoregressive Model
by Kai Jiang, Jiaxing Huang
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper provides a systematic review of autoregressive models in computer vision, exploring their applications in various tasks such as image generation, video generation, and medical image analysis. Inspired by their success in natural language processing, these models represent visual data as tokens and enable next-token predictions. The authors develop a taxonomy of existing methods, highlighting strengths, limitations, and major contributions. They also investigate recent advancements, benchmarking and discussing existing methods across various datasets. Finally, the paper outlines key challenges and promising directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autoregressive models are super smart at understanding pictures! They can create new images, videos, or even edit old ones. Researchers have been studying how these models work in computer vision, a field that combines image analysis with artificial intelligence. The main goal is to understand what these models do well and where they struggle. This paper shows what’s already known about autoregressive models and what we still need to figure out. |
Keywords
» Artificial intelligence » Autoregressive » Image generation » Natural language processing » Token
