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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 141–150 of 161 papers

TitleStatusHype
Generalized Zero-Shot Learning via Synthesized Examples—0
Generalized Zero-Shot Learning with Deep Calibration Network—0
Generalized Zero-Shot Recognition based on Visually Semantic Embedding—0
Global Semantic Consistency for Zero-Shot Learning—0
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning—0
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning—0
Hierarchical Disentanglement of Discriminative Latent Features for Zero-Shot Learning—0
Hierarchical Novelty Detection for Visual Object Recognition—0
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
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