Learning Attention Model from Human for Visuomotor Tasks

Luxin Zhang, Ruohan Zhang, Zhuode Liu, Mary M. Hayhoe, Dana H. Ballard

AAAI 2018 pp. 8181-8182

doi:10.1609/AAAI.V32I1.12147 /aaai/2018/zhang2018aaai-learning-a/

Abstract

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.

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Cite

Text

Zhang et al. "Learning Attention Model from Human for Visuomotor Tasks." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12147

Markdown

[Zhang et al. "Learning Attention Model from Human for Visuomotor Tasks." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/zhang2018aaai-learning-a/) doi:10.1609/AAAI.V32I1.12147

BibTeX

@inproceedings{zhang2018aaai-learning-a,
  title     = {{Learning Attention Model from Human for Visuomotor Tasks}},
  author    = {Zhang, Luxin and Zhang, Ruohan and Liu, Zhuode and Hayhoe, Mary M. and Ballard, Dana H.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8181-8182},
  doi       = {10.1609/AAAI.V32I1.12147},
  url       = {https://mlanthology.org/aaai/2018/zhang2018aaai-learning-a/}
}