Eye Tracking

68 papers with code • 0 benchmarks • 7 datasets

Eye tracking research

Greatest papers with code

Attention Mesh: High-fidelity Face Mesh Prediction in Real-time

google/mediapipe 19 Jun 2020

We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions.

Eye Tracking

Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction

pupil-labs/pupil 30 Apr 2014

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.

Eye Tracking Gaze Estimation

Eye Tracking for Everyone

CSAILVision/GazeCapture CVPR 2016

We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.

Eye Tracking Gaze Estimation

ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation

xucong-zhang/ETH-XGaze ECCV 2020

We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.

Eye Tracking Gaze Estimation

Predicting the Driver's Focus of Attention: the DR(eye)VE Project

ndrplz/dreyeve 10 May 2017

In this work we aim to predict the driver's focus of attention.

Eye Tracking

Towards End-to-end Video-based Eye-Tracking

swook/EVE ECCV 2020

Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors.

Eye Tracking

Attention Based Glaucoma Detection: A Large-scale Database and CNN Model

smilell/AG-CNN CVPR 2019

The attention maps of the ophthalmologists are also collected in LAG database through a simulated eye-tracking experiment.

Eye Tracking

Sequence Classification with Human Attention

coastalcph/Sequence_classification_with_human_attention CONLL 2018

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.

Abusive Language Classification +4