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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 31–40 of 161 papers

TitleStatusHype
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning—0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Evolutionary Generalized Zero-Shot LearningCode0
On the Transferability of Visual Features in Generalized Zero-Shot LearningCode0
Targeted Attention for Generalized- and Zero-Shot Learning—0
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning—0
PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot LearningCode0
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