S3OD: Towards Generalizable Salient Object Detection with Synthetic Data

• URL: http://arxiv.org/abs/2510.21605v1
• Date: Fri, 24 Oct 2025 16:10:09 GMT
• Title: S3OD: Towards Generalizable Salient Object Detection with Synthetic Data
• Authors: Orest Kupyn, Hirokatsu Kataoka, Christian Rupprecht,
• Abstract summary: We introduce S3OD, a dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline.<n>We propose a streamlined multi-mask decoder that naturally handles the inherent ambiguity in salient object detection.
• Score: 38.10559747985342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 naturally handles the inherent ambiguity in salient object detection by predicting multiple valid interpretations. Models trained solely 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.

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