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 111–120 of 161 papers

TitleStatusHype
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
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
Show:102550
← PrevPage 12 of 17Next →

No leaderboard results yet.