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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 151–161 of 161 papers

TitleStatusHype
Multi-modal Cycle-consistent Generalized Zero-Shot LearningCode0
Global Semantic Consistency for Zero-Shot Learning—0
Progressive Ensemble Networks for Zero-Shot Recognition—0
Hierarchical Novelty Detection for Visual Object Recognition—0
A Generative Approach to Zero-Shot and Few-Shot Action Recognition—0
Generalized Zero-Shot Learning via Synthesized Examples—0
Feature Generating Networks for Zero-Shot LearningCode1
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit—0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data—0
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning—0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
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