CIA: Controllable Image Augmentation Framework Based on Stable Diffusion
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910712979259392 |
|---|---|
| author | Benkedadra, Mohamed Rimez, Dany Godelaine, Tiffanie Chidambaram, Natarajan Khosroshahi, Hamed Razavi Tellez, Horacio Mancas, Matei Macq, Benoit Mahmoudi, Sidi Ahmed |
| author_facet | Benkedadra, Mohamed Rimez, Dany Godelaine, Tiffanie Chidambaram, Natarajan Khosroshahi, Hamed Razavi Tellez, Horacio Mancas, Matei Macq, Benoit Mahmoudi, Sidi Ahmed |
| contents | Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet. In order to show how CIA can be used to search for an optimal augmentation pipeline of training data, we study human object detection in a data constrained scenario, using YOLOv8n on COCO and Flickr30k datasets. We have recorded significant improvement using CIA-generated images, approaching the performances obtained when doubling the amount of real images in the dataset. Our findings suggest that our modular framework can significantly enhance object detection systems, and make it possible for future research to be done on data-constrained scenarios. The framework is available at: github.com/multitel-ai/CIA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CIA: Controllable Image Augmentation Framework Based on Stable Diffusion Benkedadra, Mohamed Rimez, Dany Godelaine, Tiffanie Chidambaram, Natarajan Khosroshahi, Hamed Razavi Tellez, Horacio Mancas, Matei Macq, Benoit Mahmoudi, Sidi Ahmed Computer Vision and Pattern Recognition Artificial Intelligence Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet. In order to show how CIA can be used to search for an optimal augmentation pipeline of training data, we study human object detection in a data constrained scenario, using YOLOv8n on COCO and Flickr30k datasets. We have recorded significant improvement using CIA-generated images, approaching the performances obtained when doubling the amount of real images in the dataset. Our findings suggest that our modular framework can significantly enhance object detection systems, and make it possible for future research to be done on data-constrained scenarios. The framework is available at: github.com/multitel-ai/CIA. |
| title | CIA: Controllable Image Augmentation Framework Based on Stable Diffusion |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.16128 |