Learning Gaussian Representation for Eye Fixation Prediction
Peipei Song Jing Zhang Piotr Koniusz Nick Barnes
Existing eye fixation prediction methods perform the mapping from input
images to the corresponding dense fixation maps generated from raw fixation
points. However, due to the stochastic nature of human fixation, the generated
dense fixation maps may be a less-than-ideal representation of human fixation.
To provide a robust fixation model, we introduce Gaussian Representation for
eye fixation modeling. Specifically, we propose to model the eye fixation map
as a mixture of probability distributions, namely a Gaussian Mixture Model. In
this new representation, we use several Gaussian distribution components as an
alternative to the provided fixation map, which makes the model more robust to
the randomness of fixation. Meanwhile, we design our framework upon some
lightweight backbones to achieve real-time fixation prediction. Experimental
results on three public fixation prediction datasets (SALICON, MIT1003,
TORONTO) demonstrate that our method is fast and effective.