  We solve the problem of salient object detection by investigating how to
expand the role of pooling in convolutional neural networks. Based on the
U-shape architecture, we first build a global guidance module (GGM) upon the
bottom-up pathway, aiming at providing layers at different feature levels the
location information of potential salient objects. We further design a feature
aggregation module (FAM) to make the coarse-level semantic information well
fused with the fine-level features from the top-down pathway. By adding FAMs
after the fusion operations in the top-down pathway, coarse-level features from
the GGM can be seamlessly merged with features at various scales. These two
pooling-based modules allow the high-level semantic features to be
progressively refined, yielding detail enriched saliency maps. Experiment
results show that our proposed approach can more accurately locate the salient
objects with sharpened details and hence substantially improve the performance
compared to the previous state-of-the-arts. Our approach is fast as well and
can run at a speed of more than 30 FPS when processing a $300 \times 400$
image. Code can be found at http://mmcheng.net/poolnet/.
