Enhancing Object Detection Training via Joint Image-Annotation Generation

Incorporating generated annotated data into training sets can improve object detection. Prior approaches either condition image generation on annotation layouts, limiting diversity and often causing misalignment, or generate images independently and annotate them afterward, reducing accuracy. We introduce a diffusion model that jointly generates images and annotations, enabling their co-evolution and mutual dependency throughout the process. This design achieves tight image-annotation alignment and produces diverse scenarios beyond the original training set, enhancing object detection performance when used in training.