Summary of Cia: Controllable Image Augmentation Framework Based on Stable Diffusion, by Mohamed Benkedadra et al.
CIA: Controllable Image Augmentation Framework Based on Stable Diffusion
by Mohamed Benkedadra, Dany Rimez, Tiffanie Godelaine, Natarajan Chidambaram, Hamed Razavi Khosroshahi, Horacio Tellez, Matei Mancas, Benoit Macq, Sidi Ahmed Mahmoudi
First submitted to arxiv on: 25 Nov 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 proposed CIA pipeline offers a modular approach to generating synthetic images for dataset augmentation, filtering out low-quality samples, and forcing the existence of specific patterns in generated images using Stable Diffusion and ControlNet. The authors demonstrate the effectiveness of this framework by applying it to object detection tasks on COCO and Flickr30k datasets, achieving significant improvements in performance comparable to doubling the amount of real images in the dataset. This work highlights the potential for CIA-generated images to enhance object detection systems and facilitate research in data-constrained scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make computer vision tasks better by generating fake images that can help train models. They created a pipeline called CIA, which makes synthetic images using a technique called Stable Diffusion, filters out bad images, and adds specific patterns. This helps improve object detection in situations where there’s not enough real data. The results show that this approach can be just as good as having twice the amount of real data! This is important because it allows researchers to work with limited data. |
Keywords
» Artificial intelligence » Diffusion » Object detection
