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 with no code
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning
Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image.
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning
However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images.
Data-Free Generalized Zero-Shot Learning
Firstly, to recover the virtual features of the base data, we model the CLIP features of base class images as samples from a von Mises-Fisher (vMF) distribution based on the pre-trained classifier.
SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning
This approach generates discriminative classes, effectively classifying both seen and unseen classes.
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
Zero-shot learning (ZSL) is a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges remain.
Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning
In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features.
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning
Generalized zero-shot learning(GZSL) aims to classify samples from seen and unseen labels, assuming unseen labels are not accessible during training.
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training.
Synthetic Sample Selection for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain in computer vision, owing to its capability to recognize objects that have not been seen during training.
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery
Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition.
