Eye tracking research
We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions.
Commercial head-mounted eye trackers provide useful features to customers in industry and research but are expensive and rely on closed source hardware and software.
This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks.
In this work we aim to predict the driver's focus of attention.
A comparison against Wang et al. shows that our method advances the state of the art in 3D eye tracking using a single RGB camera.
We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.
The attention maps of the ophthalmologists are also collected in LAG database through a simulated eye-tracking experiment.
Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP.
Eye tracking spreads through a vast area of applications from ophthalmology, assistive technologies to gaming and virtual reality.