Webly Supervised Semantic Segmentation

Abstract

We propose a weakly supervised semantic segmentation algorithm that uses image tags for supervision. We apply the tags in queries to collect three sets of web images, which encode the clean foregrounds, the common backgrounds, and realistic scenes of the classes. We introduce a novel three-stage training pipeline to progressively learn semantic segmentation models. We first train and refine a class-specific shallow neural network to obtain segmentation masks for each class. The shallow neural networks of all classes are then assembled into one deep convolutional neural network for end-to-end training and testing. Experiments show that our method notably outperforms previous state-of-the-art weakly supervised semantic segmentation approaches on the PASCAL VOC 2012 segmentation benchmark. We further apply the class-specific shallow neural networks to object segmentation and obtain excellent results.