Relation-based Discriminative Cooperation Network for Zero-Shot Classification

• The discriminative visual embedding preserves the information of image features by separating inter-classes and clustering intra-classes with a margin. semantic acts as pivot regularization to ensure cooperated structures representative utilizing relations between classes. Extensive experimental evaluation on multiple datasets, including large scale ImageNet shows that proposed model performs favorably against state-of-the-art ZSL methods. Zero-shot learning (ZSL) aims assign category corresponding relevant label unseen sample based relationship learned features. However, most typical models faced domain bias problem, which leads or test samples being easily misclassified into seen training categories. To handle this we propose relation-based cooperation network (RDCN) for in work. effectively utilize robust metric space spanned semantics help set relations. On other hand, devise relation measure embedded semantics, validation will guide module learn more information. At last, RDCN is validated six benchmarks, extensive experiments demonstrate superiority method over existing traditional zero-shot setting realistic generalized setting.