The modern object detection heavily relies on deep learning models trained end-to-end with larger and more diverse datasets. Data augmentation offers a way to boost performance without adding new annotations. AWS AI’s research explores generative data augmentation using diffusion models and CLIP, achieving significant improvements in object detection accuracy. For more details, refer to the paper.
Advanced AI Data Augmentation for Object Detection
Modern object detection algorithms rely on deep learning models trained with large, diverse annotated datasets. However, training object detection models is more time-consuming and expensive than picture classification due to the need for precise bounding boxes surrounding items in images. Generative data augmentation can significantly enhance model performance without the need for additional human annotations.
Data Augmentation Methods
Data augmentation is a technique to increase training instances without adding new annotations. Conventional data augmentation involves manipulating images by rotating, resizing, or flipping them. Generative data augmentation, on the other hand, provides augmented samples with more variety, realism, and fresh visual characteristics for improved performance in vision tasks.
Advanced Generative Data Augmentation
AWS AI has conducted a study to explore generative data augmentation for object detection using diffusion models, aiming to create high-quality bounding box annotations without human annotations. These models utilize visual priors and configurable diffusion models to generate new objects, lighting, or styles inside bounding boxes, resulting in a substantial improvement in detection performance.
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