Summary of Automatic Scene Generation: State-of-the-art Techniques, Models, Datasets, Challenges, and Future Prospects, by Awal Ahmed Fime et al.
Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects
by Awal Ahmed Fime, Saifuddin Mahmud, Arpita Das, Md. Sunzidul Islam, Hong-Hoon Kim
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The survey provides a comprehensive review of automatic scene generation techniques that leverage machine learning, deep learning, embedded systems, and natural language processing. The paper categorizes models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey explores how machines can generate scenes for various applications like robotics, recreation, visual representation, training, simulation, education, and more. It’s about using machine learning techniques like deep learning, embedded systems, and natural language processing to make this happen. The paper shows different models that do this, grouped into four main types: VAEs, GANs, Transformers, and Diffusion Models. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Natural language processing
