  In the current salient object detection network, the most popular method is
using U-shape structure. However, the massive number of parameters leads to
more consumption of computing and storage resources which are not feasible to
deploy on the limited memory device. Some others shallow layer network will not
maintain the same accuracy compared with U-shape structure and the deep network
structure with more parameters will not converge to a global minimum loss with
great speed. To overcome all of these disadvantages, we proposed a new deep
convolution network architecture with three contributions: (1) using smaller
convolution neural networks (CNNs) to compress the model in our improved
salient object features compression and reinforcement extraction module
(ISFCREM) to reduce parameters of the model. (2) introducing channel attention
mechanism in ISFCREM to weigh different channels for improving the ability of
feature representation. (3) applying a new optimizer to accumulate the
long-term gradient information during training to adaptively tune the learning
rate. The results demonstrate that the proposed method can compress the model
to 1/3 of the original size nearly without losing the accuracy and converging
faster and more smoothly on six widely used datasets of salient object
detection compared with the others models. Our code is published in
https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salient-object-detection.git
