  Fully convolutional networks have shown outstanding performance in the
salient object detection (SOD) field. The state-of-the-art (SOTA) methods have
a tendency to become deeper and more complex, which easily homogenize their
learned deep features, resulting in a clear performance bottleneck. In sharp
contrast to the conventional ``deeper'' schemes, this paper proposes a
``wider'' network architecture which consists of parallel sub networks with
totally different network architectures. In this way, those deep features
obtained via these two sub networks will exhibit large diversity, which will
have large potential to be able to complement with each other. However, a large
diversity may easily lead to the feature conflictions, thus we use the dense
short-connections to enable a recursively interaction between the parallel sub
networks, pursuing an optimal complementary status between multi-model deep
features. Finally, all these complementary multi-model deep features will be
selectively fused to make high-performance salient object detections. Extensive
experiments on several famous benchmarks clearly demonstrate the superior
performance, good generalization, and powerful learning ability of the proposed
wider framework.
