Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …

Open vocabulary models (eg CLIP) have shown strong performance on zero-shot
classification through their ability generate embeddings for each class based on their …

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 …

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 …

Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can
recognize objects from both seen and unseen classes. Due to the absence of unseen …

Abstract Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge
transferred from the seen domain, relying on the intrinsic interactions between visual and …

Zero-shot learning (ZSL) is an extreme case of transfer learning that aims to recognize
samples (eg, images) of unseen classes relying on a train-set covering only seen classes …

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never
appeared during training. One of the most effective and widely used semantic information for …

Training the multi-label image recognition models with partial labels, in which merely some
labels are known while others are unknown for each image, is a considerably challenging …