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This paper introduces TF-SSD, a novel training-free approach for co-salient object detection (CoSOD) that leverages Vision Foundation Models (VFMs) like SAM and DINO. TF-SSD uses SAM to generate candidate masks, then filters them using a quality mask generator and DINO-based intra-image saliency filter, followed by an inter-image prototype selector to refine the selection across the image group. Experiments demonstrate that TF-SSD significantly outperforms existing training-free methods, achieving a 13.7% improvement.
Skip the training data: a synergy between SAM and DINO lets you outperform existing co-salient object detection methods without any training.
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.
