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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 41–50 of 161 papers

TitleStatusHype
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning—0
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
SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning—0
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning—0
Attribute-Aware Representation Rectification for Generalized Zero-Shot LearningCode0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning—0
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