  Compared with the conventional hand-crafted approaches, the deep learning
based methods have achieved tremendous performance improvements by training
exquisitely crafted fancy networks over large-scale training sets. However, do
we really need large-scale training set for salient object detection (SOD)? In
this paper, we provide a deeper insight into the interrelationship between the
SOD performances and the training sets. To alleviate the conventional demands
for large-scale training data, we provide a feasible way to construct a novel
small-scale training set, which only contains 4K images. Moreover, we propose a
novel bi-stream network to take full advantage of our proposed small training
set, which is consisted of two feature backbones with different structures,
achieving complementary semantical saliency fusion via the proposed gate
control unit. To our best knowledge, this is the first attempt to use a
small-scale training set to outperform state-of-the-art models which are
trained on large-scale training sets; nevertheless, our method can still
achieve the leading state-of-the-art performance on five benchmark datasets.
