Fast: Feature Aggregation for Detecting Salient Object in Real-Time

This paper introduces a method named FAST for real-time salient object detection with an extremely efficient CNN architecture. Our proposed network starts from a single lightweight backbone and aggregates discriminative features through network-level and phase-level respectively. Based on the multi-scale feature propagation, FAST substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and performance. To better preserve object boundaries, we also explore the complementary between salient object information and edge information within our lightweight architecture. Extensive evaluations and analysis demonstrate that the proposed algorithm achieves the leading accuracy performance with real-time speed (186fps) which is significantly faster than the existing state-of-the-art methods.