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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
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation—0
PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning—0
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning—0
Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General FrameworkCode0
Audio-visual Generalized Zero-shot Learning the Easy Way—0
CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned RepresentationCode0
Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models—0
CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification—0
Dual Expert Distillation Network for Generalized Zero-Shot LearningCode0
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