We address the task of weakly-supervised few-shot image classification and segmentation,
by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed …

Object detection is a fundamental task in computer vision that involves accurately locating
and classifying objects within images or video frames. In remote sensing, this task is …

Gradient-based meta-learning algorithms offer promising solutions to the challenge of swift
adaptation to new tasks, especially when faced with limited sample data. One pivotal …

Nowadays, users demand for increased personalization of vision systems to localize and
identify personal instances of objects (eg, my dog rather than dog) from a few-shot dataset …

Few-shot image classification aims to learn an image classifier using only a small set of
labeled examples per class. A recent research direction for improving few-shot classifiers …

Microservice-based systems (MSS) may experience failures in various fault categories due
to their complex and dynamic nature. To effectively handle failures, AIOps tools utilize trace …

Zero-shot learning (ZSL) is implemented by transferring knowledge from seen classes to
unseen classes through embedding space or feature generation. However, the embedding …

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data
relationship. It is critical in many machine learning, pattern recognition and data mining …