  We address the problem of generalized zero-shot semantic segmentation (GZS3)
predicting pixel-wise semantic labels for seen and unseen classes. Most GZS3
methods adopt a generative approach that synthesizes visual features of unseen
classes from corresponding semantic ones (e.g., word2vec) to train novel
classifiers for both seen and unseen classes. Although generative methods show
decent performance, they have two limitations: (1) the visual features are
biased towards seen classes; (2) the classifier should be retrained whenever
novel unseen classes appear. We propose a discriminative approach to address
these limitations in a unified framework. To this end, we leverage visual and
semantic encoders to learn a joint embedding space, where the semantic encoder
transforms semantic features to semantic prototypes that act as centers for
visual features of corresponding classes. Specifically, we introduce
boundary-aware regression (BAR) and semantic consistency (SC) losses to learn
discriminative features. Our approach to exploiting the joint embedding space,
together with BAR and SC terms, alleviates the seen bias problem. At test time,
we avoid the retraining process by exploiting semantic prototypes as a
nearest-neighbor (NN) classifier. To further alleviate the bias problem, we
also propose an inference technique, dubbed Apollonius calibration (AC), that
modulates the decision boundary of the NN classifier to the Apollonius circle
adaptively. Experimental results demonstrate the effectiveness of our
framework, achieving a new state of the art on standard benchmarks.
