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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 110 of 161 papers

TitleStatusHype
Leveraging Foundation Models for Zero-Shot IoT SensingCode1
Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal ModelsCode1
Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class NamesCode1
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
Improving Zero-Shot Generalization for CLIP with Synthesized PromptsCode1
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot LearningCode1
Learn to Adapt for Generalized Zero-Shot Text ClassificationCode1
Zero-Shot Logit AdjustmentCode1
Unseen Classes at a Later Time? No ProblemCode1
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
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