  Salient object detection (SOD), which aims to find the most important region
of interest and segment the relevant object/item in that area, is an important
yet challenging vision task. This problem is inspired by the fact that human
seems to perceive main scene elements with high priorities. Thus, accurate
detection of salient objects in complex scenes is critical for human-computer
interaction. In this paper, we present a novel feature learning framework for
SOD, in which we cast the SOD as a pixel-wise classification problem. The
proposed framework utilizes a densely hierarchical feature fusion network,
named HyperFusion-Net, automatically predicts the most important area and
segments the associated objects in an end-to-end manner. Specifically, inspired
by the human perception system and image reflection separation, we first
decompose input images into reflective image pairs by content-preserving
transforms. Then, the complementary information of reflective image pairs is
jointly extracted by an interweaved convolutional neural network (ICNN) and
hierarchically combined with a hyper-dense fusion mechanism. Based on the fused
multi-scale features, our method finally achieves a promising way of predicting
SOD. As shown in our extensive experiments, the proposed method consistently
outperforms other state-of-the-art methods on seven public datasets with a
large margin.
