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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
CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification—0
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition—0
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning—0
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification—0
Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning—0
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning—0
Distinguishing Unseen From Seen for Generalized Zero-Shot Learning—0
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning—0
Domain segmentation and adjustment for generalized zero-shot learning—0
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