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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 51–60 of 161 papers

TitleStatusHype
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
Generalized Zero-Shot Learning via Synthesized Examples—0
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning—0
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention—0
CLAREL: Classification via retrieval loss for zero-shot learning—0
Domain segmentation and adjustment for generalized zero-shot learning—0
From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process—0
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning—0
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