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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 101–110 of 161 papers

TitleStatusHype
Transferable Contrastive Network for Generalized Zero-Shot Learning—0
Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners—0
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning—0
Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning—0
Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning—0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features—0
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit—0
Global Semantic Consistency for Zero-Shot Learning—0
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning—0
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