Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection
methods are typically inadequate in generalizability, with a tendency to overfit to image …

Abstract Zero-Shot Learning (ZSL) involves transferring knowledge from seen classes to
unseen classes by establishing connections between visual and semantic spaces …

Abstract Text-to-image (T2I) generative models have recently emerged as a powerful tool
enabling the creation of photo-realistic images and giving rise to a multitude of applications …

Abstract Generalized Zero-shot Learning (GZSL) seeks to identify objects from both seen
and unseen classes, relying solely on labeled samples from the seen classes. One of …

Few-shot learning (FSL) is designed to explore the distribution of novel categories from a
few samples. It is a challenging task since the classifier is usually susceptible to over-fitting …

Gearboxes, fundamental components in the domains of manufacturing, transportation, and
aerospace apparatus, are highly susceptible to impairments. The emerging technique of non …

With the goal to detect both the object categories appearing in the training phase and those
never have been observed before testing, zero-shot object detection (ZSD) becomes a …

The long-tail distribution of items is common in recommendation systems. However, due to
the limited interaction records of long-tail items, recommending them to users significantly …

Zero-shot learning (ZSL) facilitates the transfer of knowledge from seen to unseen
categories through high-dimensional vectors that capture both known and unknown class …

To tackle the challenge of recognizing images of unseen attribute-object compositions,
Compositional Zero-Shot Learning (CZSL) methods have been previously addressed …