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
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.
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning
In AAM, the correlation between each patch feature and the synthetic image attribute is used as the importance weight for each patch.
Evolutionary Generalized Zero-Shot Learning
In this work, we propose a novel Evolutionary Generalized Zero-Shot Learning setting, which (i) avoids the domain shift problem in inductive GZSL, and (ii) is more in line with the needs of real-world deployments than transductive GZSL.
Targeted Attention for Generalized- and Zero-Shot Learning
We are able to achieve state-of-the-art performance on the CUB200 and Cars196 datasets in the ZSL setting compared to recent works, with NMI (normalized mutual inference) of 63. 27 and top-1 of 61. 04 for CUB200, and NMI 66. 03 with top-1 82. 75% in Cars196.
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning
Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process.
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
In order to distill discriminative visual words from noisy documents, we introduce a new cross-modal attention module that learns fine-grained interactions between image patches and document words.
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning
One of the recent developments in deep learning is generalized zero-shot learning (GZSL), which aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided.
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning
To address this issue, we propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification
Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images.
