Machine learning (ML), especially deep neural networks, has achieved great success, but
many of them often rely on a number of labeled samples for supervision. As sufficient …

The emergence of unknown diseases is often with few or no samples available. Zero-shot
learning and few-shot learning have promising applications in medical image analysis. In …

Despite the advances made in visual object recognition, state-of-the-art deep learning
models struggle to effectively recognize novel objects in a few-shot setting where only a …

This experience report describes ongoing research into developing a machine learning
classifier that can track high quality written feedback during peer review sessions. The …

Generalized compositional zero-shot learning means to learn composed concepts of
attribute-object pairs in a zero-shot fashion, where a model is trained on a set of seen …

1 Background The rapid and dynamic pace of Artificial Intelligence (AI) and Machine
Learning (ML) developments is revolutionizing the insurance sector, especially in property …

Multi-label zero-shot learning (ML-ZSL), with the difficulty of both multi-label learning and
zero-shot learning, aims to recognize various unseen objects that are not observed during …

In the era of big data, we have access to various sources of potentially unlimited data, but
collecting labels for those data is still very costly for computer vision. For example, object …