  Deep neural networks (DNNs) based saliency detection approaches have succeed
in recent years, and improved the performance by a great margin via
increasingly sophisticated network architecture. Despite the performance
improvement, the computational cost is excessively high for such low level
visual task. In this work, we propose a light-weighted saliency detection
approach with distinctively lower runtime memory cost and model size. We
evaluated the performance of our approach on multiple benchmark datasets, and
achieved competitive results comparing with state-of-the-art methods on
multiple metrics. We also evaluated the computational cost of our approach with
multiple measurements. The runtime memory cost of our approach is 42 to 99
times fewer comparing with the previous DNNs based methods. The model size of
our approach is 63 to 129 times smaller, and takes less than 1 Megabytes
storage space with out any deep compression technique.
