Indoor human activity recognition is actively studied as part of creating various intelligent
systems with applications in smart home and office, smart health, internet of things, etc …

The quantification of cranial sexual dimorphism (CSD) among modern humans is relevant in
evolutionary studies of morphological variation and in a forensic context. Despite the …

Open-world recognition (OWR) is an important field of research that strives to develop
machine learning models capable of identifying and learning new classes as they appear …

Unsupervised deep metric learning (UDML) focuses on learning a semantic representation
space using only unlabeled data. This challenging problem requires accurately estimating …

To perform object-specific tasks on the desktop, robots need to perceive different objects.
The challenge is to calculate the pixel-wise mask for each object, even in the presence of …

It is not so easy to obtain the complete fault data for mechanical equipment toward certain
variable working conditions. Traditional deep learning algorithms based on limited training …

In the field of Human-Robot Interaction (HRI), many researchers study shared control
systems. Shared control is when a person and agent both contribute to the performance of a …

Permanent magnets are essential components in a range of applications from robotics to
energy harvesting devices. Computing the field distribution of permanent magnets is crucial …

Automatically understanding and describing the visual content of videos in natural language
is a challenging task in computer vision. Existing approaches are often designed to describe …

Learning from multiview skeletal video data is difficult due to overlapping joints across
views. In this work, we propose to overcome the above challenge by pairing views into …