Prototypical Matching and Open Set Rejection for Zero-Shot Semantic Segmentation
Hui Zhang, Henghui Ding
Abstract
The DCNN methods in addressing semantic segmentation demand vast amount of pixel-wise annotated training samples. In this work, we present zero-shot semantic segmentation, which aims to identify not only the seen classes contained in training but also the novel classes that have never been seen. We adopt a stringent inductive setting in which only the instances of seen classes are accessible during training. We propose an open-aware prototypical matching approach to accomplish the segmentation. The prototypical way extracts the visual representations by a set of prototypes, making it convenient and flexible to add new unseen classes. A prototype projection is trained to map the semantic representations towards prototypes based on seen instances, and will generate prototypes for unseen classes. Moreover, an open-set rejection is utilized to detect objects that do not belong to any seen classes, which greatly reduces the misclassification of unseen objects into seen classes due to the lack of seen training instances. We apply the framework on two segmentation datasets, Pascal VOC 2012 and Pascal Context, and achieve impressively state-of-the-art performance.