Semantic combined network for zero‐shot scene parsing

Abstract

2020) 'Semantic combined network for zero-shot scene parsing . ', IET image processing., 14 (4). 757 -765. The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.Abstract: Recently, image-based scene parsing has attracted increasing attention due to its wide application. However, conventional models can only be valid on images with the same domain of the training set, and are typically trained using discrete and meaningless labels. Inspired by the traditional zero shot learning methods which employ an auxiliary side information to bridge the source and target domains, we propose a novel framework called Semantic Combined Network (SCN), which aims at learning a scene parsing model only from the images of the seen classes while targeting on the unseen ones. In addition, with the assist of semantic embeddings of classes, our SCN can further improve the performances of traditional fully supervised scene parsing methods. Extensive experiments are conducted on the dataset Cityscapes, and the results show that our SCN can perform well on both Zero Shot Scene Parsing (ZSSP) and Generalized ZSSP (GZSSP) settings based on several state-of-the-art scene parsing architectures. Furthermore, we test our model under the traditional fully supervised setting and the results show that our SCN can also significantly improve the performances of the original network models.