  Existing CNNs-Based RGB-D salient object detection (SOD) networks are all
required to be pretrained on the ImageNet to learn the hierarchy features which
helps provide a good initialization. However, the collection and annotation of
large-scale datasets are time-consuming and expensive. In this paper, we
utilize self-supervised representation learning (SSL) to design two pretext
tasks: the cross-modal auto-encoder and the depth-contour estimation. Our
pretext tasks require only a few and unlabeled RGB-D datasets to perform
pretraining, which makes the network capture rich semantic contexts and reduce
the gap between two modalities, thereby providing an effective initialization
for the downstream task. In addition, for the inherent problem of cross-modal
fusion in RGB-D SOD, we propose a consistency-difference aggregation (CDA)
module that splits a single feature fusion into multi-path fusion to achieve an
adequate perception of consistent and differential information. The CDA module
is general and suitable for cross-modal and cross-level feature fusion.
Extensive experiments on six benchmark datasets show that our self-supervised
pretrained model performs favorably against most state-of-the-art methods
pretrained on ImageNet. The source code will be publicly available at
\textcolor{red}{\url{https://github.com/Xiaoqi-Zhao-DLUT/SSLSOD}}.
