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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 21–30 of 161 papers

TitleStatusHype
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot LearningCode1
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot LearningCode1
Class Normalization for (Continual)? Generalized Zero-Shot LearningCode1
Zero-Shot Learning with Common Sense Knowledge GraphsCode1
From Generalized zero-shot learning to long-tail with class descriptorsCode1
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot LearningCode1
Latent Embedding Feedback and Discriminative Features for Zero-Shot ClassificationCode1
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
Feature Generating Networks for Zero-Shot LearningCode1
Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning—0
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