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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 31–40 of 161 papers

TitleStatusHype
A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Zero-Shot Recognition via Optimal Transport—0
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition—0
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures—0
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning—0
CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification—0
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification—0
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning—0
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning—0
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