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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 71–80 of 161 papers

TitleStatusHype
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
Generalized Zero-Shot Recognition based on Visually Semantic Embedding—0
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning—0
Generalized Zero-Shot Learning with Deep Calibration Network—0
A Generative Approach to Zero-Shot and Few-Shot Action Recognition—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
Improving Generalized Zero-Shot Learning by Semantic Discriminator—0
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning—0
Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning—0
Generalized Zero-Shot Learning via Synthesized Examples—0
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