Object detection model building for re-train using Stable Diffusion and image generation interpretation techniques

Abstract

Stable Diffusion, which synthesizes images by sentences, has been attracting attention. Interpretation techniques for Stable Diffusion have also emerged to indicate the parts of the image that are related to each word in the sentence. In this paper, we propose a method for building an object detection model, which is used re-training, with Stable Diffusion and the interpretation technique. Our proposed method uses Stable Diffusion to synthesize a lot of images of domains similar to the desired object, and automatically annotates the objects in the images with the interpretation technique. Evaluation result shows that re-training with object detection models built by our method resulted in higher detection accuracy than using models trained on the COCO dataset.