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
Latest papers
Recognition of Unseen Bird Species by Learning from Field Guides
Illustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species.
Zero-Shot Logit Adjustment
As a consequence of our derivation, the aforementioned two properties are incorporated into the classifier training as seen-unseen priors via logit adjustment.
Deconstructed Generation-Based Zero-Shot Model
cdb342/dgz
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Recent research on Generalized Zero-Shot Learning (GZSL) has focused primarily on generation-based methods.
Unseen Classes at a Later Time? No Problem
sumitramalagi/unseen-classes-at-a-later-time
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CVPR 2022
Secondly, we introduce a unified feature-generative framework for CGZSL that leverages bi-directional incremental alignment to dynamically adapt to addition of new classes, with or without labeled data, that arrive over time in any of these CGZSL settings.
A Gating Model for Bias Calibration in Generalized Zero-shot Learning
Also, the two-stream autoencoder works as a unified framework for the gating model and the unseen expert, which makes the proposed method computationally efficient.
Bias-Eliminated Semantic Refinement for Any-Shot Learning
When training samples are scarce, the semantic embedding technique, ie, describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects.
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning
cetinsamet/closed-form-sample-probing
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ICLR 2022
In our approach, at each generative model update step, we fit a task-specific closed-form ZSL model from generated samples, and measure its loss on novel samples all within the compute graph, a procedure that we refer to as sample probing.
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
valeoai/3DGenZ
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While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification.
FREE: Feature Refinement for Generalized Zero-Shot Learning
shiming-chen/FREE
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ICCV 2021
FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples.
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
Here, we propose a multi-label generalized zero shot learning (CXR-ML-GZSL) network that can simultaneously predict multiple seen and unseen diseases in CXR images.
