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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Human Gaze Assisted Artificial Intelligence: A Review.

Ruohan Zhang¹, Akanksha Saran¹, Bo Liu¹

¹The University of Texas at Austin.

IJCAI : Proceedings of the Conference

|September 9, 2020

Summary

This summary is machine-generated.

Human gaze tracking offers insights into cognitive states, driving research in AI fields like computer vision and robotics. This work synthesizes gaze data collection, behavior modeling, and application utilization to foster interdisciplinary communication.

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Area of Science:

Computer Vision
Natural Language Processing
Robotics
Cognitive Science

Background:

Human gaze is a rich indicator of internal cognitive states.
Recent years have seen a surge in gaze-related research across multiple disciplines.
Interdisciplinary communication in gaze research is currently limited.

Purpose of the Study:

To provide a comprehensive overview of human gaze research.
To synthesize efforts in data collection, behavior modeling, and application of gaze information.
To enhance communication and collaboration between diverse research areas.

Main Methods:

Literature review of gaze-related research.
Categorization of research efforts into data collection, modeling, and application.
Analysis of interdisciplinary connections and communication gaps.

Main Results:

Identified key methodologies in human gaze data collection and modeling.
Highlighted diverse applications of gaze information across computer vision, NLP, decision learning, and robotics.
Established a framework for understanding the current landscape of gaze research.

Conclusions:

Effective communication is crucial for advancing human-centered artificial intelligence.
Future research should focus on bridging interdisciplinary gaps in gaze analysis.
Potential exists for novel applications through enhanced collaboration in human gaze research.