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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 101–110 of 161 papers

TitleStatusHype
A Generative Approach to Zero-Shot and Few-Shot Action Recognition—0
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning—0
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification—0
Zero-Shot Recognition via Optimal Transport—0
A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition—0
A Simple Approach for Zero-Shot Learning based on Triplet Distribution Embeddings—0
Audio-visual Generalized Zero-shot Learning the Easy Way—0
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning—0
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings—0
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning—0
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