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Talent Scout
Z
Zhongxi Lu
University of Leicester
V
Vincent G. Zakka
Aston University
L
Luis J. Manso
Aston University
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Founder's Pitch
"GazeMoE leverages a Mixture-of-Experts model to provide state-of-the-art gaze target estimation for robotics and HCI applications."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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arXiv Paper
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Why It Matters
This research addresses the need for accurate gaze target estimation in real-world scenarios, enabling improved human-computer interaction and understanding of human cognition through non-invasive means.
Product Angle
The solution can be packaged as a SaaS tool for companies needing to integrate gaze tracking in their robotics, augmented reality, or customer analytics platforms.
Disruption
It could replace less accurate gaze tracking solutions that do not leverage multi-modal cues or advanced Mixture-of-Experts architectures, offering higher performance and versatility across various deployment scenarios.
Product Opportunity
The market is substantial, involving sectors like robotics, automotive (for driver monitoring), retail (consumer analytics), and healthcare (autism research), where accurate gaze tracking is crucial. Companies in these sectors would likely pay for such a technology to enhance their products and services.
Use Case Idea
Deploy GazeMoE in retail environments to analyze customer interest on shelves or products in real-time, aiding in consumer behavior analytics and shelf management.
Science
The paper proposes GazeMoE, a model using Mixture-of-Experts layers to dynamically route and analyze visual cues such as eye landmarks, head poses, gestures and scene context to accurately estimate gaze direction from images, using DINOv2 as a frozen foundation model for feature extraction.
Method & Eval
The model was tested on several benchmark datasets, showing superior performance in terms of prediction accuracy and robustness in diverse and out-of-distribution visual environments compared to existing methods.
Caveats
The model requires fine-tuning and may not be as effective with low-quality input data. Additionally, reliance on large pre-trained models like DINOv2 may introduce limitations in terms of model updates and availability.
Author Intelligence
Zhuangzhuang Dai
LEAD Aston University
z.dai1@aston.ac.uk
Zhongxi Lu
University of Leicester
Vincent G. Zakka
Aston University
Luis J. Manso
Aston University
Jose M Alcaraz Calero
Aston University
Chen Li
Aalborg University