Generalized Zero-Shot Learning
55 papers with code • 12 benchmarks • 10 datasets
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.
Benchmarks
These leaderboards are used to track progress in Generalized Zero-Shot Learning
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Datasets
CUB-200-2011
ScanNet
Oxford 102 Flower
SemanticKITTI
S3DIS
AwA
OntoNotes 5.0
AwA2
aPY
SUN Attribute
Most implemented papers
Feature Generating Networks for Zero-Shot Learning
akku1506/Feature-Generating-Networks-for-ZSL
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CVPR 2018
Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task.
Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples.
Class Normalization for (Continual)? Generalized Zero-Shot Learning
universome/czsl
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Normalization techniques have proved to be a crucial ingredient of successful training in a traditional supervised learning regime.
Contrastive Embedding for Generalized Zero-Shot Learning
Hanzy1996/CE-GZSL
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CVPR 2021
To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework.
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
edgarschnfld/CADA-VAE-PyTorch
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Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning
Chenxingyu1990/A-Boundary-Based-Out-of-Distribution-Classifier-for-Generalized-Zero-Shot-Learning
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ECCV 2020
Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.
A Deep Dive into Adversarial Robustness in Zero-Shot Learning
MKYucel/adversarial_robustness_zsl
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In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes.
Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models
However, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP.
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only.
Multi-modal Cycle-consistent Generalized Zero-Shot Learning
rfelixmg/frwgan-eccv18
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ECCV 2018
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.
