Multimodal learning provides a path to fully utilize all types of information related to the
modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a …

Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …

Driver behavior recognition has become one of the most important tasks for intelligent
vehicles. This task, however, is very challenging since the background contents in real-world …

Driver observation models are rarely deployed under perfect conditions. In practice,
illumination, camera placement and type differ from the ones present during training and …

High-quality online open courses have a wide audience. To further improve the quality of
these courses, it is critical to analyze the teaching behaviors in class, which are the …

While deep Convolutional Neural Networks (CNNs) have become front-runners in the field
of driver observation, they are often perceived as black boxes due to their end-to-end nature …

Driver distraction behavior recognition is currently a significant study area that involves
analyzing and identifying various movements, actions, and patterns exhibited by drivers …

Human affect recognition is a well-established research area with numerous applications,
eg in psychological care, but existing methods assume that all emotions-of-interest are given …

Driver activity classification is crucial for ensuring road safety, with applications ranging from
driver assistance systems to autonomous vehicle control transitions. In this paper, we …

With the rise of autonomous vehicles, drivers are gradually being liberated from the
traditional roles behind steering wheels. Driver behavior cognition is significant for …