Deeper Look at Image Salient Object Detection: Bi-Stream Network With a Small Training Dataset
PubDate: December 2020
Teams: Stony Brook University；Beihang University；Qingdao University
Writers: Zhenyu Wu; Shuai Li; Chenglizhao Chen; Aimin Hao; Hong Qin
Compared with the conventional hand-crafted approaches, the deep learning based ISOD (image salient object detection) models 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 ISOD? In this article, we provide a deeper insight into the interrelationship between the ISOD performance and the training data. 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 4 K images. To take full advantage of this new set, we propose a novel bi-stream network consisting of two different feature backbones. Benefit from the proposed gate control unit, this bi-stream network is able to achieve complementary fusion status for its subbranches. To our best knowledge, this is the first attempt to use a small-scale training set to compete with other large-scale ones; nevertheless, our method can still achieve the leading SOTA performance on all tested benchmark datasets. Both the code and dataset are publicly available at https://github.com/wuzhenyubuaa/TSNet.