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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 81–90 of 161 papers

TitleStatusHype
Multi-modal Ensemble Classification for Generalized Zero Shot Learning—0
Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning—0
Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning—0
Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis—0
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning—0
PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning—0
Rectification-based Knowledge Retention for Continual Learning—0
Relation-based Generalized Zero-shot Classification with the Domain Discriminator on the shared representation—0
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning—0
SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning—0
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