  With the goal of identifying pixel-wise salient object regions from each
input image, salient object detection (SOD) has been receiving great attention
in recent years. One kind of mainstream SOD methods is formed by a bottom-up
feature encoding procedure and a top-down information decoding procedure. While
numerous approaches have explored the bottom-up feature extraction for this
task, the design on top-down flows still remains under-studied. To this end,
this paper revisits the role of top-down modeling in salient object detection
and designs a novel densely nested top-down flows (DNTDF)-based framework. In
every stage of DNTDF, features from higher levels are read in via the
progressive compression shortcut paths (PCSP). The notable characteristics of
our proposed method are as follows. 1) The propagation of high-level features
which usually have relatively strong semantic information is enhanced in the
decoding procedure; 2) With the help of PCSP, the gradient vanishing issues
caused by non-linear operations in top-down information flows can be
alleviated; 3) Thanks to the full exploration of high-level features, the
decoding process of our method is relatively memory efficient compared against
those of existing methods. Integrating DNTDF with EfficientNet, we construct a
highly light-weighted SOD model, with very low computational complexity. To
demonstrate the effectiveness of the proposed model, comprehensive experiments
are conducted on six widely-used benchmark datasets. The comparisons to the
most state-of-the-art methods as well as the carefully-designed baseline models
verify our insights on the top-down flow modeling for SOD. The code of this
paper is available at https://github.com/new-stone-object/DNTD.
