  Given the semantic descriptions of classes, Zero-Shot Learning (ZSL) aims to
recognize unseen classes without labeled training data by exploiting semantic
information, which contains knowledge between seen and unseen classes. Existing
ZSL methods mainly use vectors to represent the embeddings to the semantic
space. Despite the popularity, such vector representation limits the
expressivity in terms of modeling the intra-class variability for each class.
We address this issue by leveraging the use of distribution embeddings. More
specifically, both image embeddings and class embeddings are modeled as
Gaussian distributions, where their similarity relationships are preserved
through the use of triplet constraints. The key intuition which guides our
approach is that for each image, the embedding of the correct class label
should be closer than that of any other class label. Extensive experiments on
multiple benchmark data sets show that the proposed method achieves highly
competitive results for both traditional ZSL and more challenging Generalized
Zero-Shot Learning (GZSL) settings.

Computer Vision and Pattern Recognition;Machine Learning