  Visual saliency detection tries to mimic human vision psychology which
concentrates on sparse, important areas in natural image. Saliency prediction
research has been traditionally based on low level features such as contrast,
edge, etc. Recent thrust in saliency prediction research is to learn high level
semantics using ground truth eye fixation datasets. In this paper we present,
WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using
domain specific pre-learing on ImageNet for saliency prediction using a light
weight CNN architecture. The paper proposes a two step hierarchical learning,
in which the first step is to develop a framework for weakly pre-training on a
large scale dataset such as ImageNet which is void of human eye fixation maps.
The second step refines the pre-trained model on a limited set of ground truth
fixations. Analysis of loss on iSUN and SALICON datasets reveal that
pre-trained network converges much faster compared to randomly initialized
network. WEPSAM also outperforms some recent state-of-the-art saliency
prediction models on the challenging MIT300 dataset.

Computer Vision and Pattern Recognition