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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 111–120 of 161 papers

TitleStatusHype
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings—0
From Generalized zero-shot learning to long-tail with class descriptorsCode1
Generalized Zero-Shot Learning Via Over-Complete DistributionCode0
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot LearningCode1
Latent Embedding Feedback and Discriminative Features for Zero-Shot ClassificationCode1
Domain segmentation and adjustment for generalized zero-shot learning—0
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning—0
Zero-Shot Recognition via Optimal Transport—0
Generalized Zero-shot ICD Coding—0
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