SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 161 papers

TitleStatusHype
Zero-Shot Learning by Harnessing Adversarial SamplesCode0
Synthetic Sample Selection for Generalized Zero-Shot Learning—0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
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
Show:102550
← PrevPage 6 of 17Next →

No leaderboard results yet.