Video understanding has long suffered from reliance on large labeled datasets, motivating
research into zero-shot learning. Recent progress in language modeling presents …

Zero-shot learning aims to recognize objects from novel concepts through the model trained
on seen class data and assisted by the semantic descriptions. Though it breaks the serious …

The key challenge of zero-shot learning (ZSL) is to sufficiently disentangle each latent
attribute from the class-level semantic annotations of images, thereby achieving a desirable …

The zero-shot semantic segmentation requires models with a strong image understanding
ability. The majority of current solutions are based on direct map** or generation. These …

Abstract Zero-Shot Learning (ZSL) aims at generalizing the classification experience from
seen classes to unseen classes with auxiliary side information, among which word vectors of …

Many approaches in generalized zero-shot learning (GZSL) rely on cross-modal map**
between the image feature space and the class embedding space, which achieves …

In generalized zero-shot learning (GZSL), it is required to identify seen and unseen samples
under the condition that only seen classes can be obtained during training. Recent methods …

Traditional zero-shot learning aims to use the trained model to accurately classify samples
from unseen classes, while for the more difficult task of generalized zero-shot learning, the …

Zero-shot learning aims to identify unseen (novel) objects, using only labeled samples from
seen (base) classes. Existing methods usually learn visual-semantic interactions or …

Generalized zero-shot learning (GZSL) is an important research area in image computing,
video processing, multimedia understanding, and other visual computing tasks. GZSL …