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Related Experiment Video

Updated: Jul 31, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

LiteGaze: Neural architecture search for efficient gaze estimation.

Xinwei Guo¹, Yong Wu², Jingjing Miao³

¹School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.

|May 1, 2023

Summary

LiteGaze efficiently designs deep learning models for gaze estimation on edge devices using neural architecture search. This framework enables real-time eye-tracking applications by optimizing computational costs.

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Gaze estimation is crucial for human-centered vision applications like HCI and VR.
Deep learning models for gaze estimation face deployment challenges on edge devices due to high computational costs and resource limitations.

Purpose of the Study:

To propose LiteGaze, a novel deep learning framework for efficient gaze estimation architecture search.
To enable real-time gaze estimation on resource-constrained edge devices.

Main Methods:

Utilizes neural architecture search (NAS) inspired by the once-for-all model.
Decouples model training and architecture search into two stages: supernet training and sub-network selection.
Selects specialized sub-networks based on specific efficiency constraints.

Main Results:

Demonstrates superior performance over previous methods in extensive experiments on two gaze estimation datasets.
Successfully advances real-time gaze estimation capabilities for edge devices.

Conclusions:

LiteGaze provides an efficient solution for deploying deep learning-based gaze estimation models on edge devices.
The proposed NAS framework effectively balances accuracy and computational efficiency for real-time applications.