Combining ontology and reinforcement learning for zero-shot classification

Zero-Shot Classification (ZSC) has received much attention recently in computer vision research. Traditional classifiers are unable to handle ZSC because test data labels are significantly different from training data labels. Attribute-based methods have long dominated ZSC. However, classical attribute-based methods fail to distinguish between discriminative attributes and non-discriminative attributes and do not distinguish the different contributions each attribute makes to classification. We propose CORL (Combining Ontology and Reinforcement Learning) for ZSC. CORL first obtains hierarchical classification rules from attribute annotations of object classes based on ontology. These rules contain only discriminative attributes. Reinforcement learning is used to adaptively determine the discriminative degrees of the rules. The most discriminative rules are then selected for ZSC. Experiments on three benchmark datasets showed that CORL achieved higher accuracies than baseline classifiers. This suggests that CORL effectively discovers the most discriminative rules for ZSC.