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Generalized Zero-Shot Learning

In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.

Papers

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Showing 81–90 of 161 papers

TitleStatusHype
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning—0
Adversarial Training of Variational Auto-encoders for Continual Zero-shot Learning(A-CZSL)Code0
Isometric Propagation Network for Generalized Zero-shot Learning—0
Semantic Borrowing for Generalized Zero-Shot Learning—0
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action RecognitionCode1
Semantics Disentangling for Generalized Zero-Shot LearningCode1
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition—0
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning—0
Adaptive and Generative Zero-Shot LearningCode1
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification—0
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