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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 11–20 of 161 papers

TitleStatusHype
CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification—0
Dual Expert Distillation Network for Generalized Zero-Shot LearningCode0
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning—0
Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal ModelsCode1
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning—0
Less but Better: Enabling Generalized Zero-shot Learning Towards Unseen Domains by Intrinsic Learning from Redundant LLM SemanticsCode0
Data Distribution Distilled Generative Model for Generalized Zero-Shot RecognitionCode0
Data-Free Generalized Zero-Shot LearningCode0
Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class NamesCode1
SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning—0
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