  Salient object detection (SOD) has been in the spotlight recently, yet has
been studied less for high-resolution (HR) images. Unfortunately, HR images and
their pixel-level annotations are certainly more labor-intensive and
time-consuming compared to low-resolution (LR) images and annotations.
Therefore, we propose an image pyramid-based SOD framework, Inverse Saliency
Pyramid Reconstruction Network (InSPyReNet), for HR prediction without any of
HR datasets. We design InSPyReNet to produce a strict image pyramid structure
of saliency map, which enables to ensemble multiple results with pyramid-based
image blending. For HR prediction, we design a pyramid blending method which
synthesizes two different image pyramids from a pair of LR and HR scale from
the same image to overcome effective receptive field (ERF) discrepancy. Our
extensive evaluations on public LR and HR SOD benchmarks demonstrate that
InSPyReNet surpasses the State-of-the-Art (SotA) methods on various SOD metrics
and boundary accuracy.
