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 …
Boosting zero-shot learning via contrastive optimization of attribute representations
Zero-shot learning (ZSL) aims to recognize classes that do not have samples in the training
set. One representative solution is to directly learn an embedding function associating visual …
set. One representative solution is to directly learn an embedding function associating visual …
Text2Model: Text-based Model Induction for Zero-shot Image Classification
We address the challenge of building task-agnostic classifiers using only text descriptions,
demonstrating a unified approach to image classification, 3D point cloud classification, and …
demonstrating a unified approach to image classification, 3D point cloud classification, and …
Text2Model: Model Induction for Zero-shot Generalization Using Task Descriptions
We study the problem of generating a training-free task-dependent visual classifier from text
descriptions without visual samples. This\textit {Text-to-Model}(T2M) problem is closely …
descriptions without visual samples. This\textit {Text-to-Model}(T2M) problem is closely …