A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
Towards zero-shot learning: A brief review and an attention-based embedding network
Zero-shot learning (ZSL), an emerging topic in recent years, targets at distinguishing unseen
class images by taking images from seen classes for training the classifier. Existing works …
class images by taking images from seen classes for training the classifier. Existing works …
Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes
In this work we study the problem of using referential language to identify common objects in
real-world 3D scenes. We focus on a challenging setup where the referred object belongs to …
real-world 3D scenes. We focus on a challenging setup where the referred object belongs to …
Region graph embedding network for zero-shot learning
Most of the existing Zero-Shot Learning (ZSL) approaches learn direct embeddings from
global features or image parts (regions) to the semantic space, which, however, fail to …
global features or image parts (regions) to the semantic space, which, however, fail to …
Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification
Although deep learning has revolutionized remote sensing (RS) image scene classification,
current deep learning-based approaches highly depend on the massive supervision of …
current deep learning-based approaches highly depend on the massive supervision of …
Domain-aware visual bias eliminating for generalized zero-shot learning
Generalized zero-shot learning aims to recognize images from seen and unseen domains.
Recent methods focus on learning a unified semantic-aligned visual representation to …
Recent methods focus on learning a unified semantic-aligned visual representation to …
Invertible zero-shot recognition flows
Deep generative models have been successfully applied to Zero-Shot Learning (ZSL)
recently. However, the underlying drawbacks of GANs and VAEs (eg, the hardness of …
recently. However, the underlying drawbacks of GANs and VAEs (eg, the hardness of …
Learning deep cross-modal embedding networks for zero-shot remote sensing image scene classification
Due to its wide applications, remote sensing (RS) image scene classification has attracted
increasing research interest. When each category has a sufficient number of labeled …
increasing research interest. When each category has a sufficient number of labeled …
Bi-directional distribution alignment for transductive zero-shot learning
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain
shift, where the true and learned data distributions for the unseen classes do not match …
shift, where the true and learned data distributions for the unseen classes do not match …
Self-supervised generalized zero shot learning for medical image classification using novel interpretable saliency maps
In many real world medical image classification settings, access to samples of all disease
classes is not feasible, affecting the robustness of a system expected to have high …
classes is not feasible, affecting the robustness of a system expected to have high …