  Visual attention is a fundamental mechanism in the human brain, and it
inspires the design of attention mechanisms in deep neural networks. However,
most of the visual attention studies adopted eye-tracking data rather than the
direct measurement of brain activity to characterize human visual attention. In
addition, the adversarial relationship between the attention-related objects
and attention-neglected background in the human visual system was not fully
exploited. To bridge these gaps, we propose a novel brain-inspired adversarial
visual attention network (BI-AVAN) to characterize human visual attention
directly from functional brain activity. Our BI-AVAN model imitates the biased
competition process between attention-related/neglected objects to identify and
locate the visual objects in a movie frame the human brain focuses on in an
unsupervised manner. We use independent eye-tracking data as ground truth for
validation and experimental results show that our model achieves robust and
promising results when inferring meaningful human visual attention and mapping
the relationship between brain activities and visual stimuli. Our BI-AVAN model
contributes to the emerging field of leveraging the brain's functional
architecture to inspire and guide the model design in artificial intelligence
(AI), e.g., deep neural networks.
