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 …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Recently, open-vocabulary image classification by vision language pre-training has
demonstrated incredible achievements, that the model can classify arbitrary categories …

The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge
between visual and attribute features on seen classes, and thus achieving a desirable …

The learning of the deep networks largely relies on the data with human-annotated labels. In
some label insufficient situations, the performance degrades on the decision boundary with …

When labeled training data is scarce, a promising data augmentation approach is to
generate visual features of unknown classes using their attributes. To learn the class …

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic
knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute …

Zero-shot point cloud segmentation aims to make deep models capable of recognizing
novel objects in point cloud that are unseen in the training phase. Recent trends favor the …

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 …

Zero-shot learning (ZSL) aims to classify images from unseen categories, by merely utilizing
seen class images as the training data. Existing works on ZSL mainly leverage the global …