# TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detection Source: [TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detection]() Collection index: [llms.txt]() Data updated: 2026-04-03T05:00:04.568Z Authors: Zhijin He, Shuo Jin, Siyue Yu, Shuwei Wu, Bingfeng Zhang, Li Yu, Jimin Xiao Published: 2026-04-01T06:47:14.000Z ## Abstract Co-salient Object Detection (CoSOD) aims to segment salient objects that consistently appear across a group of related images. Despite the notable progress achieved by recent training-based approaches, they still remain constrained by the closed-set datasets and exhibit limited generalization. However, few studies explore the potential of Vision Foundation Models (VFMs) to address CoSOD, which demonstrate a strong generalized ability and robust saliency understanding. In this paper, we investigate and leverage VFMs for CoSOD, and further propose a novel training-free method, TF-SSD, through the synergy between SAM and DINO. Specifically, we first utilize SAM to generate comprehensive raw proposals, which serve as a candidate mask pool. Then, we introduce a quality mask generator to filter out redundant masks, thereby acquiring a refined mask set. Since this generator is built upon SAM, it inherently lacks semantic understanding of saliency. To this end, we adopt an intra-image saliency filter that employs DINO's attention maps to identify visually salient masks within individual images. Moreover, to extend saliency understanding across group images, we propose an inter-image prototype selector, which computes similarity scores among cross-image prototypes to select masks with the highest score. These selected masks serve as final predictions for CoSOD. Extensive experiments show that our TF-SSD outperforms existing methods (e.g., 13.7\\% gains over the recent training-free method). Codes are available at https://github.com/hzz-yy/TF-SSD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Co-salient Object Detection on CoCA v1 (test)](): Co-Salient Object Detection; CoCA v1 (test); Mean Absolute Error (MAE): 0.077. - [Co-salient Object Detection on CoSal 2015 v1 (test)](): Co-Salient Object Detection; CoSal 2015 v1 (test); Mean Absolute Error (MAE): 8.9. - [Co-salient Object Detection on CoSOD3k v1 (test)](): Co-Salient Object Detection; CoSOD3k v1 (test); MAE: 9. ## Original sources and implementation [Original paper]() [PDF]()