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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 151–161 of 161 papers

TitleStatusHype
Improving Generalized Zero-Shot Learning by Semantic Discriminator—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Isometric Propagation Network for Generalized Zero-shot Learning—0
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision—0
Learning Graph-Based Priors for Generalized Zero-Shot Learning—0
Learning shared manifold representation of images and attributes for generalized zero-shot learning—0
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Learning the Compositional Spaces for Generalized Zero-shot Learning—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Learning without Seeing nor Knowing: Towards Open Zero-Shot Learning—0
Learn to Adapt for Generalized Zero-Shot Text Classification—0
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