  As an essential problem in computer vision, salient object detection (SOD)
has attracted an increasing amount of research attention over the years. Recent
advances in SOD are predominantly led by deep learning-based solutions (named
deep SOD). To enable in-depth understanding of deep SOD, in this paper, we
provide a comprehensive survey covering various aspects, ranging from algorithm
taxonomy to unsolved issues. In particular, we first review deep SOD algorithms
from different perspectives, including network architecture, level of
supervision, learning paradigm, and object-/instance-level detection. Following
that, we summarize and analyze existing SOD datasets and evaluation metrics.
Then, we benchmark a large group of representative SOD models, and provide
detailed analyses of the comparison results. Moreover, we study the performance
of SOD algorithms under different attribute settings, which has not been
thoroughly explored previously, by constructing a novel SOD dataset with rich
attribute annotations covering various salient object types, challenging
factors, and scene categories. We further analyze, for the first time in the
field, the robustness of SOD models to random input perturbations and
adversarial attacks. We also look into the generalization and difficulty of
existing SOD datasets. Finally, we discuss several open issues of SOD and
outline future research directions.
