  UNet-based methods have shown outstanding performance in salient object
detection (SOD), but are problematic in two aspects. 1) Indiscriminately
integrating the encoder feature, which contains spatial information for
multiple objects, and the decoder feature, which contains global information of
the salient object, is likely to convey unnecessary details of non-salient
objects to the decoder, hindering saliency detection. 2) To deal with ambiguous
object boundaries and generate accurate saliency maps, the model needs
additional branches, such as edge reconstructions, which leads to increasing
computational cost. To address the problems, we propose a context fusion
decoder network (CFDN) and near edge weighted loss (NEWLoss) function. The CFDN
creates an accurate saliency map by integrating global context information and
thus suppressing the influence of the unnecessary spatial information. NEWLoss
accelerates learning of obscure boundaries without additional modules by
generating weight maps on object boundaries. Our method is evaluated on four
benchmarks and achieves state-of-the-art performance. We prove the
effectiveness of the proposed method through comparative experiments.
