Top Down Fusing Multi level Contextual Features for Salient Object Detection
Recently, benefiting from the fast development of deep convolutional neural networks, salient object detection (SOD) has achieved gratifying performance in a variety of challenging scenarios. Among them, how to learn more discriminative features plays a key role. In this paper, we propose a novel network architecture that progressively fuses the rich multi level contextual features from top to bottom to learn a more effective feature presentation for robust SOD. Concretely, we first design a multi receptive field block (MRFB) to capture multi scale contextual information. Then, we develop a feature fusion block that progressively fuses different outputs of MRFBs from top to bottom, which can effectively filter out the non complementary parts of the high level and low level features. Afterwards, we leverage a refinement residual block to refine the results further. Finally, we leverage an edge aware loss as an aid to guide the network to learn more sharpen details of the salient objects. The whole network is trained end to end without any pre processing and post processing. Exhaustive evaluations on six benchmark datasets demonstrate superiority of the proposed method against state of the arts in terms of all metrics.
PRCV