Webly-supervised learning for salient object detection

摘要

End-to-end training of a deep CNN-Based model for salient object detection usually requires a huge number of training samples with pixel-level annotations, which are costly and time-consuming to obtain. In this paper, we propose an approach that can utilize large amounts of web data for learning a deep salient object detection model. With thousands of images collected from the Web, we first employ several bottom-up saliency detection techniques to generate salient object masks for all images, and then use a novel quality evaluation method to pick out a subset of images with reliable masks for training. After that, we develop a self-training approach to boost the performance of our initial network, which iterates between the network training process and the training set updating process. Importantly, different from existing webly-supervised or weakly-supervised methods, our approach is able to automatically select reliable images for network training without requiring any human intervention (e.g., dividing images into different difficulty levels).