Attribute Propagation Network for Graph Zero-shot Learning

Understand

The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training.

• To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.
• In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes.
• We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings.