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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 111–120 of 161 papers

TitleStatusHype
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention—0
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning—0
Improving Generalized Zero-Shot Learning by Semantic Discriminator—0
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings—0
Generalized Zero-Shot Learning Via Over-Complete DistributionCode0
Domain segmentation and adjustment for generalized zero-shot learning—0
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning—0
Zero-Shot Recognition via Optimal Transport—0
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