Summary of Ship in Sight: Diffusion Models For Ship-image Super Resolution, by Luigi Sigillo et al.
Ship in Sight: Diffusion Models for Ship-Image Super Resolution
by Luigi Sigillo, Riccardo Fosco Gramaccioni, Alessandro Nicolosi, Danilo Comminiello
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper explores the application of super-resolution techniques to enhance low-resolution images in the context of ship image generation. The authors develop a diffusion-model-based architecture that leverages text conditioning during training and is class-aware, allowing for the preservation of crucial details in generated images. They also introduce a large labeled ship dataset scraped from online sources. Experimental results show improved performance compared to state-of-the-art methods for tasks like classification and object detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves image generation by using super-resolution techniques on low-quality ship images. It creates a new way to generate high-quality pictures of ships using text information. This is important for surveillance and other applications. The authors test their method and compare it to others, showing that it works well. They also release the code so others can use it. |
Keywords
* Artificial intelligence * Classification * Diffusion model * Image generation * Object detection * Super resolution
