Semi-supervised Salient Object Detection with Effective Confidence Estimation

The success of existing salient object detection models relies on a large pixel-wise labeled training dataset. How-ever, collecting such a dataset is not only time-consuming but also very expensive. To reduce the labeling burden, we study semi-supervised salient object detection, and formulate it as an unlabeled dataset pixel-level confidence estimation problem by identifying pixels with less confident predictions. Specifically, we introduce a new latent variable model with an energy-based prior for effective latent space exploration, leading to more reliable confidence maps. With the proposed strategy, the unlabelled images can effectively participate in model training. Experimental results show that the proposed solution, using only 1/16 of the annotations from the original training dataset, achieves competitive performance compared with state-of-the-art fully supervised models.