Salient Object Detection From Unlabeled Images

Abstract

Salient object detection via deep neural networks usually needs a large amount of images with human annotation. To avoid laborious and consuming annotation, we propose a robust unsupervised salient object detection method with three stages in this work. Our method first uses unlabeled data to generate an activation map which indicates the coarse location of object. Then one scribble generation method based on the activation map is proposed, which provides foreground scribble and background scribble with high confidence. Finally, a salient object detection model is trained with the supervision of the generated scribble. Performance comparison is carried on four public datasets, showing that our method significantly outperforms state-of-the-art unsupervised methods, and also achieves better performance than some weakly supervised methods.