Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Human gaze provides valuable information on human focus and intentions, making it a
crucial area of research. Recently, deep learning has revolutionized appearance-based …

Abstract Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain
gap between a labeled source dataset and an unlabelled target dataset for regression …

Gaze target detection aims to predict the image location where the person is looking and the
probability that a gaze is out of the scene. Several works have tackled this task by regressing …

Recent advances of deep learning-based approaches have achieved remarkable
performance on appearance-based gaze estimation. However, due to the shortage of target …

Gaze estimation across domains has been explored recently because the training data are
usually collected under controlled conditions while the trained gaze estimators are used in …

Gaze estimation methods learn eye gaze from facial features. However, among rich
information in the facial image, real gaze-relevant features only correspond to subtle …

Gaze tracking estimates and tracks the user's gaze by analyzing facial or eye features, it is
an important way to realize automated vision-based interaction. This paper introduces the …

Deep neural networks have significantly improved appearance-based gaze estimation
accuracy. However, it still suffers from unsatisfactory performance when generalizing the …

The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding
human activity in the realm of egocentric vision. The majority of existing estimation methods …