Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Large, labeled datasets have driven deep learning methods to achieve expert-level
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …

As one of the most comprehensive and objective ways to describe facial expressions, the
Facial Action Coding System (FACS) has recently received significant attention. Over the …

Annotating multiple organs in medical images is both costly and time-consuming; therefore,
existing multi-organ datasets with labels are often low in sample size and mostly partially …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

Recently, multi-label learning has received much attention in the applications of image
annotation and classification. However, most existing multi-label learning methods do not …

The COVID-19 pandemic forced governments across the world to impose lockdowns to
prevent virus transmissions. This resulted in the shutdown of all economic activity and …

Facial action units (AU) are the fundamental units to decode human facial expressions. At
least three aspects affect performance of automated AU detection: spatial representation …

Abstract Facial Action Coding System is the most influential sign judgment method for facial
behavior, and it is a comprehensive and anatomical system which could encode various …