  Existing salient object detection methods are capable of predicting binary
maps that highlight visually salient regions. However, these methods are
limited in their ability to differentiate the relative importance of multiple
objects and the relationships among them, which can lead to errors and reduced
accuracy in downstream tasks that depend on the relative importance of multiple
objects. To conquer, this paper proposes a new paradigm for saliency ranking,
which aims to completely focus on ranking salient objects by their "importance
order". While previous works have shown promising performance, they still face
ill-posed problems. First, the saliency ranking ground truth (GT) orders
generation methods are unreasonable since determining the correct ranking order
is not well-defined, resulting in false alarms. Second, training a ranking
model remains challenging because most saliency ranking methods follow the
multi-task paradigm, leading to conflicts and trade-offs among different tasks.
Third, existing regression-based saliency ranking methods are complex for
saliency ranking models due to their reliance on instance mask-based saliency
ranking orders. These methods require a significant amount of data to perform
accurately and can be challenging to implement effectively. To solve these
problems, this paper conducts an in-depth analysis of the causes and proposes a
whole-flow processing paradigm of saliency ranking task from the perspective of
"GT data generation", "network structure design" and "training protocol". The
proposed approach outperforms existing state-of-the-art methods on the
widely-used SALICON set, as demonstrated by extensive experiments with fair and
reasonable comparisons. The saliency ranking task is still in its infancy, and
our proposed unified framework can serve as a fundamental strategy to guide
future work.
