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

Light-weighted Saliency Detection with Distinctively Lower Memory Cost and Model Size