  This paper focuses on the problem of visual saliency prediction, predicting
regions of an image that tend to attract human visual attention, under a
constrained computational budget. We modify and test various recent efficient
convolutional neural network architectures like EfficientNet and MobileNetV2
and compare them with existing state-of-the-art saliency models such as SalGAN
and DeepGaze II both in terms of standard accuracy metrics like AUC and NSS,
and in terms of the computational complexity and model size. We find that
MobileNetV2 makes an excellent backbone for a visual saliency model and can be
effective even without a complex decoder. We also show that knowledge transfer
from a more computationally expensive model like DeepGaze II can be achieved
via pseudo-labelling an unlabelled dataset, and that this approach gives result
on-par with many state-of-the-art algorithms with a fraction of the
computational cost and model size. Source code is available at
https://github.com/feiyanhu/FastSal.
