  It is believed that human vision system (HVS) consists of pre-attentive
process and attention process when performing salient object detection (SOD).
Based on this fact, we propose a four-stage framework for SOD, in which the
first two stages match the \textbf{P}re-\textbf{A}ttentive process consisting
of general feature extraction (GFE) and feature preprocessing (FP), and the
last two stages are corresponding to \textbf{A}ttention process containing
saliency feature extraction (SFE) and the feature aggregation (FA), namely
\textbf{PAANet}. According to the pre-attentive process, the GFE stage applies
the fully-trained backbone and needs no further finetuning for different
datasets. This modification can greatly increase the training speed. The FP
stage plays the role of finetuning but works more efficiently because of its
simpler structure and fewer parameters. Moreover, in SFE stage we design for
saliency feature extraction a novel contrast operator, which works more
semantically in contrast with the traditional convolution operator when
extracting the interactive information between the foreground and its
surroundings. Interestingly, this contrast operator can be cascaded to form a
deeper structure and extract higher-order saliency more effective for complex
scene. Comparative experiments with the state-of-the-art methods on 5 datasets
demonstrate the effectiveness of our framework.
