Diffusers Generated Unlabeled Images Improves Semi-supervised Object Detection

Zhanyun Lu, Renshu Gu, Huimin Cheng, Mingyu Xu, Peifang Xu, Yuichiro Kinoshita, Juan Ye, Gangyong Jia, Qing Wu

Abstract: In the field of object detection, particularly in medical imaging, the scarcity of data often poses a significant challenge to model performance. To address this issue, this study proposes a semi-supervised learning approach based on a generative model. We begin by fine-tuning a pre-trained generative model using our dataset to better adapt the generative model to our specific data distribution. The fine-tuned generative model is then used to generate additional unlabeled data. These generated unlabeled data, combined with the original dataset, are employed in a semi-supervised training process. Experimental results demonstrate that our method significantly enhances the performance of the object detection model, especially in scenarios with limited labeled data, such as medical imaging. By incorporating the generated unlabeled training data into the semi-supervised framework, we observed a notable improvement in model accuracy. Specifically, our experiments showed an increase of up to $6.92 \%$ after adding the generated iamges. Moreover, it is foreseeable that incorporating a higher proportion of generated unlabeled data could lead to even more significant improvements in performance.

External IDs:dblp:conf/cw/LuGCXXKYJW24