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 …

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 …

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 …

With the growing interest in pretrained vision-language models like CLIP, recent research
has focused on adapting these models to downstream tasks. Despite achieving promising …

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 …

From the beginning of zero-shot learning research, visual attributes have been shown to
play an important role. In order to better transfer attribute-based knowledge from known 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 …

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 …