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 …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

We aim at advancing open-vocabulary object detection, which detects objects described by
arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly …

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 …

Automatically detecting/segmenting object (s) that blend in with their surroundings is difficult
for current models. A major challenge is that the intrinsic similarities between such …

How to make a segmentation model efficiently adapt to a specific video as well as online
target appearance variations is a fundamental issue in the field of video object …

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 …

Few-shot semantic segmentation (FSS) aims to segment unseen class objects given very
few densely-annotated support images from the same class. Existing FSS methods find the …

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring
semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable …