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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

Showing 1120 of 161 papers

TitleStatusHype
Adaptive and Generative Zero-Shot LearningCode1
Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class NamesCode1
Latent Embedding Feedback and Discriminative Features for Zero-Shot ClassificationCode1
Learn to Adapt for Generalized Zero-Shot Text ClassificationCode1
A Review of Generalized Zero-Shot Learning MethodsCode1
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot LearningCode1
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot LearningCode1
Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal ModelsCode1
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
From Generalized zero-shot learning to long-tail with class descriptorsCode1
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