  In recent years, saliency ranking has emerged as a challenging task focusing
on assessing the degree of saliency at instance-level. Being subjective, even
humans struggle to identify the precise order of all salient instances.
Previous approaches undertake the saliency ranking by directly sorting the rank
scores of salient instances, which have not explicitly resolved the inherent
ambiguities. To overcome this limitation, we propose the ranking by partition
paradigm, which segments unordered salient instances into partitions and then
ranks them based on the correlations among these partitions. The ranking by
partition paradigm alleviates ranking ambiguities in a general sense, as it
consistently improves the performance of other saliency ranking models.
Additionally, we introduce the Dense Pyramid Transformer (DPT) to enable global
cross-scale interactions, which significantly enhances feature interactions
with reduced computational burden. Extensive experiments demonstrate that our
approach outperforms all existing methods. The code for our method is available
at \url{https://github.com/ssecv/PSR}.
