  State-of-the-art saliency prediction methods develop upon model architectures
or loss functions; while training to generate one target saliency map. However,
publicly available saliency prediction datasets can be utilized to create more
information for each stimulus than just a final aggregate saliency map. This
information when utilized in a biologically inspired fashion can contribute in
better prediction performance without the use of models with huge number of
parameters. In this light, we propose to extract and use the statistics of (a)
region specific saliency and (b) temporal order of fixations, to provide
additional context to our network. We show that extra supervision using
spatially or temporally sequenced fixations results in achieving better
performance in saliency prediction. Further, we also design novel architectures
for utilizing this extra information and show that it achieves superior
performance over a base model which is devoid of extra supervision. We show
that our best method outperforms previous state-of-the-art methods with 50-80%
fewer parameters. We also show that our models perform consistently well across
all evaluation metrics unlike prior methods.

Computer Vision and Pattern Recognition;Machine Learning