Mixed-dual-head Meets Box Priors: A Robust Framework for Semi-supervised Segmentation

Chenshu Chen, Tao Liu, Wenming Tan, Shiliang Pu

Abstract: As it is costly to densely annotate large scale datasets for supervised semantic segmentation, extensive semi-supervised methods have been proposed. However, the accuracy, stability and flexibility of existing methods are still far from satisfactory. In this paper, we propose an effective and flexible framework for semi-supervised semantic segmentation using a small set of fully labeled images and a set of weakly labeled images with bounding box labels. In our framework, position and class priors are designed to guide the annotation network to predict accurate pseudo masks for weakly labeled images, which are used to train the segmentation network. We also propose a mixed-dual-head training method to reduce the interference of label noise while enabling the training process more stable. Experiments on PASCAL VOC 2012 show that our method achieves state-of-the-art performance and can achieve competitive results even with very few fully labeled images. Furthermore, the performance can be further boosted with extra weakly labeled images from COCO dataset.