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}.

近年来，显著性排序成为一项有挑战性的任务，专注于评估实例级别的显著性程度。我们提出了一种基于分区的排序方法，通过将无序的显著实例分割成分区，并根据这些分区之间的相关性进行排序，从而解决了排序歧义问题。此外，我们引入了密集金字塔变换器（DPT）来实现全局跨尺度交互，在减少计算负担的同时显著增强了特征交互。大量实验证明我们的方法优于所有现有方法。

具有密集金字塔变换器的分割显著性排名