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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 2130 of 161 papers

TitleStatusHype
Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class NamesCode1
Improving Zero-Shot Generalization for CLIP with Synthesized PromptsCode1
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
Adaptive and Generative Zero-Shot LearningCode1
From Generalized zero-shot learning to long-tail with class descriptorsCode1
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest RadiographsCode1
Semantics Disentangling for Generalized Zero-Shot LearningCode1
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action RecognitionCode1
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot LearningCode1
A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition0
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