Abstract
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which
is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a
framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model
learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With
such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for
solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method