  Attention mechanisms are widely used in salient object detection models based
on deep learning, which can effectively promote the extraction and utilization
of useful information by neural networks. However, most of the existing
attention modules used in salient object detection are input with the processed
feature map itself, which easily leads to the problem of `blind
overconfidence'. In this paper, instead of applying the widely used
self-attention module, we present an output-guided attention module built with
multi-scale outputs to overcome the problem of `blind overconfidence'. We also
construct a new loss function, the intractable area F-measure loss function,
which is based on the F-measure of the hard-to-handle area to improve the
detection effect of the model in the edge areas and confusing areas of an
image. Extensive experiments and abundant ablation studies are conducted to
evaluate the effect of our methods and to explore the most suitable structure
for the model. Tests on several data sets show that our model performs very
well, even though it is very lightweight.
