mlsanityData to 14 Oct 2026

S3OD: Towards Generalizable Salient Object Detection with Synthetic Data

arXiv 2510.21605 ↗First posted 24 Oct 2025cs.CVcited by 6

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Abstract (arXiv)

Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present a method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-aware architecture. We introduce S3OD, a dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline that extracts labels from diffusion and DINO-v3 features. The iterative generation framework prioritizes challenging categories based on model performance. We propose a streamlined multi-mask decoder that handles the inherent ambiguity in salient object detection by predicting multiple valid interpretations. Models trained only on synthetic data achieve 20-50% error reduction in cross-dataset generalization, while fine-tuned versions reach state-of-the-art performance across DIS and HR-SOD benchmarks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 49 of its 76 references that are in the map. First posted on arXiv 24 Oct 2025.

Builds on

49 of 76 references.

References · 49

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37 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.