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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 121–130 of 161 papers

TitleStatusHype
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning—0
Dual Progressive Prototype Network for Generalized Zero-Shot Learning—0
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning—0
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning—0
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning—0
Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning—0
Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021—0
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