  Recent research advances in salient object detection (SOD) could largely be
attributed to ever-stronger multi-scale feature representation empowered by the
deep learning technologies. The existing SOD deep models extract multi-scale
features via the off-the-shelf encoders and combine them smartly via various
delicate decoders. However, the kernel sizes in this commonly-used thread are
usually "fixed". In our new experiments, we have observed that kernels of small
size are preferable in scenarios containing tiny salient objects. In contrast,
large kernel sizes could perform better for images with large salient objects.
Inspired by this observation, we advocate the "dynamic" scale routing (as a
brand-new idea) in this paper. It will result in a generic plug-in that could
directly fit the existing feature backbone. This paper's key technical
innovations are two-fold. First, instead of using the vanilla convolution with
fixed kernel sizes for the encoder design, we propose the dynamic pyramid
convolution (DPConv), which dynamically selects the best-suited kernel sizes
w.r.t. the given input. Second, we provide a self-adaptive bidirectional
decoder design to accommodate the DPConv-based encoder best. The most
significant highlight is its capability of routing between feature scales and
their dynamic collection, making the inference process scale-aware. As a
result, this paper continues to enhance the current SOTA performance. Both the
code and dataset are publicly available at
https://github.com/wuzhenyubuaa/DPNet.
