Salient Object Detection (SOD) aims to identify and segment prominent regions within a scene. Traditional models rely on manually annotated pseudo labels with precise pixel-level accuracy, which is time-consuming. We developed a low-cost, high-precision annotation method by leveraging large foundation models to address the challenges. Specifically, we use a weakly supervised approach to guide large models in generating pseudo-labels through textual prompts. Since large models do not effectively focus on the salient regions of images, we manually annotate a subset of text to fine-tune the model. Based on this approach, which enables precise and rapid generation of pseudo-labels, we introduce a new dataset, BDS-TR. Compared to the previous DUTS-TR dataset, BDS-TR is more prominent in scale and encompasses a wider variety of categories and scenes. This expansion will enhance our model's applicability across a broader range of scenarios and provide a more comprehensive foundational dataset for future SOD research. Additionally, we present an edge decoder based on dynamic upsampling, which focuses on object edges while gradually recovering image feature resolution. Comprehensive experiments on five benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches and also surpasses several existing fully-supervised SOD methods. The code and results will be made available.

本研究解决了显著物体检测中传统方法依赖手动标注伪标签的时间消耗问题。创新性地采用弱监督方法，通过文本提示引导大型模型生成伪标签，同时细调模型以更好地聚焦于图像的显著区域。我们的方法通过推出包含更多类别和场景的新数据集BDS-TR，显著提升了模型的适应性，并在多个基准数据集上超越了现有最先进的方法，推动了未来显著物体检测研究的发展。

通过大型基础模型的知识蒸馏提升显著物体检测