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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 41–50 of 161 papers

TitleStatusHype
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning—0
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning—0
Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation—0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data—0
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning—0
Dual Progressive Prototype Network for Generalized Zero-Shot Learning—0
CLAREL: Classification via retrieval loss for zero-shot learning—0
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings—0
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention—0
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