  In image recognition, there are many cases where training samples cannot
cover all target classes. Zero-shot learning (ZSL) utilizes the class semantic
information to classify samples of the unseen categories that have no
corresponding samples contained in the training set. In this paper, we propose
an end-to-end framework, called Global Semantic Consistency Network (GSC-Net
for short), which makes complete use of the semantic information of both seen
and unseen classes, to support effective zero-shot learning. We also adopt a
soft label embedding loss to further exploit the semantic relationships among
classes. To adapt GSC-Net to a more practical setting, Generalized Zero-shot
Learning (GZSL), we introduce a parametric novelty detection mechanism. Our
approach achieves the state-of-the-art performance on both ZSL and GZSL tasks
over three visual attribute datasets, which validates the effectiveness and
advantage of the proposed framework.
