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 …

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 …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

The deep learning methods in addressing semantic segmentation typically demand vast
amount of pixel-wise annotated training samples. In this work, we present zero-shot …

We study universal zero-shot segmentation in this work to achieve panoptic, instance, and
semantic segmentation for novel categories without any training samples. Such zero-shot …

Zero-shot instance segmentation aims to detect and precisely segment objects of unseen
categories without any training samples. Since the model is trained on seen categories …

Abstract Machine learning (ML) models have proved efficient in classifying data samples
into their respective categories. The standard ML evaluation methodology assumes that test …

Internet of Things (IoT) aims at providing connectivity between every computing entity.
However, this facilitation is also leading to more cyber threats which may exploit the …

Generalized zero-shot learning (GZSL) poses a challenging problem in that it aims to
recognize both seen classes that have appeared in the training stage and unseen classes …

Generalised zero-shot learning (GZSL) is defined by a training process containing a set of
visual samples from seen classes and a set of semantic samples from seen and unseen …