Summary of Instruction-guided Editing Controls For Images and Multimedia: a Survey in Llm Era, by Thanh Tam Nguyen and Zhao Ren and Trinh Pham and Thanh Trung Huynh and Phi Le Nguyen and Hongzhi Yin and Quoc Viet Hung Nguyen
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
by Thanh Tam Nguyen, Zhao Ren, Trinh Pham, Thanh Trung Huynh, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multimedia (cs.MM)
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 explores the intersection of large language models (LLMs) and multimodal learning in visual editing, enabling users to create and manipulate digital content with minimal technical expertise. By synthesizing over 100 publications, the survey examines methods such as generative adversarial networks and diffusion models that facilitate fine-grained control over visual content. The authors discuss practical applications across domains like fashion, 3D scene manipulation, and video synthesis, highlighting increased accessibility and alignment with human intuition. The paper also compares existing literature, emphasizing LLM-empowered editing, and identifies key challenges to stimulate further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models and learning about multiple things can help people create and change digital pictures without needing a lot of technical knowledge. It talks about different ways that computers can do this, like making fake images or changing the way pictures look. The authors show how these ideas can be used in real-life situations, like making fashion designs or changing 3D pictures. They also compare what other people have written about this topic and say what problems need to be solved. |
Keywords
* Artificial intelligence * Alignment * Diffusion
