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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 61–70 of 161 papers

TitleStatusHype
Closed-form Sample Probing for Learning Generative Models in Zero-shot LearningCode0
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point CloudsCode1
Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021—0
FREE: Feature Refinement for Generalized Zero-Shot LearningCode1
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest RadiographsCode1
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision—0
Mitigating Generation Shifts for Generalized Zero-Shot LearningCode0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning—0
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning—0
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