2013
CVPR
CVPR 2013
Studying Relationships between Human Gaze, Description, and Computer Vision
Abstract
We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance. In this paper, we conduct experiments to better understand the relationship between images, the eye movements people make while viewing images, and how people construct natural language to describe images. We explore these relationships in the context of two commonly used computer vision datasets. We then further relate human cues with outputs of current visual recognition systems and demonstrate prototype applications for gaze-enabled detection and annotation.
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Conference Pioneer
— CVPR 2013
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision and Interdisciplinary and Machine Learning
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Trend Setter
— Data Augmentation
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Keyword Pioneer
— natural language description
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Hot Topic Early Bird
— natural language
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio