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 …

Multimodal learning provides a path to fully utilize all types of information related to the
modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a …

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and
unseen classes, when only the labeled examples from seen classes are provided. Recent …

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts
dedicated to overcoming the problems of visual-semantic domain gaps and seen-unseen …

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by
generative adversarial networks (GAN). To compensate for the lack of training samples in …

We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-
Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by …

Zero-shot learning strives to classify unseen categories for which no data is available during
training. In the generalized variant, the test samples can further belong to seen or unseen …

Open-vocabulary instance segmentation aims at segmenting novel classes without mask
annotations. It is an important step toward reducing laborious human supervision. Most …

We address the problem of generalized zero-shot semantic segmentation (GZS3) predicting
pixel-wise semantic labels for seen and unseen classes. Most GZS3 methods adopt a …

We introduce a simple yet effective episode-based training framework for zero-shot learning
(ZSL), where the learning system requires to recognize unseen classes given only the …