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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 131–140 of 161 papers

TitleStatusHype
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention—0
CLAREL: Classification via retrieval loss for zero-shot learning—0
From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation—0
Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders—0
Generalized Zero-shot ICD Coding—0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
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