We are thrilled to announce the groundbreaking research from AWS AI that introduces a powerful machine learning data augmentation pipeline leveraging controllable diffusion models and CLIP for enhanced object detection! This revolutionary technique has the potential to quickly improve performance in downstream tasks with both sparse and complete datasets.
By utilizing diffusion-based inpainting techniques to create objects within specified bounding boxes, the team was able to generate high-quality bounding box annotation while incorporating new objects, lighting, and styles into the image. The researchers also used visual priors such as HED boundaries and semantic segmentation masks extracted from the original annotated dataset in conjunction with configurable diffusion models for guided text-to-image generation.
The team conducted comprehensive experiments using various downstream datasets, conventional settings with the PASCAL VOC dataset, and few-shot settings with the MSCOCO dataset, and the results were remarkable. Their method yielded an improvement of 18.0%, 15.6%, and 15.9% in the YOLOX detector’s mAP result for the COCO 5/10/30-shot dataset, 2.9% for the complete PASCAL VOC dataset, and an average improvement of 12.4% for downstream datasets.
These findings are incredibly exciting, and the team has hinted that the proposed method can be used with other data augmentation approaches for even further performance boosts. To learn more about this revolutionary research, be sure to check out the paper and join our 35k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, LinkedIn Group, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more!
