Learning Attention Model From Human for Visuomotor Tasks

A wealth of information regarding intelligent decision making is conveyed by human gaze and visual attention, hence, modeling and exploiting such information might be a promising way to strengthen algorithms like deep reinforcement learning. We collect high-quality human action and gaze data while playing Atari games. Using these data, we train a deep neural network that can predict human gaze positions and visual attention with high accuracy.

Authors

• Luxin Zhang
• Ruohan Zhang
• Zhuode Liu
• Mary Hayhoe
• Dana Ballard

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