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}.

翻译：近年来，显著性排序作为一项聚焦于实例级显著性程度评估的任务而兴起。由于具有主观性，即使是人类也难以准确识别所有显著性实例的精确顺序。现有方法通常通过直接对显著性实例的排序分数进行排序来处理显著性排序问题，但未能明确解决其中固有的歧义性。为克服这一局限，我们提出了一种"基于分区的排序"范式，该方法将未排序的显著性实例划分为多个分区，随后根据这些分区间的关联性对其进行排序。这种"基于分区的排序"范式在广义上缓解了排序歧义性问题，因其能持续提升其他显著性排序模型的性能。此外，我们引入了密集金字塔变换器（DPT），该模块通过促进全局跨尺度交互，在显著增强特征交互的同时降低了计算负担。大量实验证明，我们的方法优于所有现有方法。本方法的代码可在 \url{https://github.com/ssecv/PSR} 获取。