Partitioned Saliency Ranking with Dense Pyramid Transformers
This work addresses the problem of ambiguous saliency ranking for computer vision applications, offering an incremental improvement over prior approaches.
The paper tackles the challenging task of saliency ranking by proposing a ranking by partition paradigm that segments salient instances into partitions and ranks them based on correlations, which consistently improves performance over existing methods, as demonstrated in extensive experiments.
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}.