# Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction

Source: [Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction](<https://www.sota2.com/research/paper/68fa69191e209131cea2034b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:53:51.580Z

Authors: Wolfgang Fuhl, Yao Rong, Enkelejda Kasneci

Published: 2020-02-17T06:57:09.000Z

## Abstract

In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data. We also use these networks for reconstruction, and in conjunction with a variational auto-encoder to generate eye movement data. The first improvement of our approach is that no input window is necessary, due to the use of fully convolutional networks and therefore any input size can be processed directly. The second improvement is that the used and generated data is raw eye tracking data (position X, Y and time) without preprocessing. This is achieved by pre-initializing the filters in the first layer and by building the input tensor along the z axis. We evaluated our approach on three publicly available datasets and compare the results to the state of the art.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2002.10905>)

[PDF](<https://arxiv.org/pdf/2002.10905.pdf>)