NETWORK FOR SALIENCY DETECTION BASED ON RESIDUAL FUSION OF FEATURES
Benefitting from convolution neural network with supervised training,recent works of saliency detection achieves good results.However,it is still a core issue that how to effectively use the salient features in the model.We believe that the fusion of different levels of saliency feature information can complement each other and promote effect of the final prediction.In this paper,a network framework based on local information residual fusion is proposed.This framework was to fuse the features of the local convolution layer in the form of residual error,so as to avoid the risk of introducing noise due to too many sampling operations.The fused new feature map was transmitted from deep layer to shallow layer progressively,and the final prediction result was obtained.
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