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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 71–80 of 161 papers

TitleStatusHype
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition—0
Zero-Shot Recognition via Optimal Transport—0
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models—0
CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification—0
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning—0
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning—0
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning—0
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