用修复模型的无遮挡偏见实现无需调参的完整物体分割
Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models
- 利用修复模型自动补全被遮挡区域，直接生成完整物体掩码
- 在五个数据集上平均比现有方法高5.3%的分割准确率
- 无需训练或微调，适合快速部署和跨场景应用
非可视分割旨在预测物体可见与被遮挡区域的分割掩码。现有方法多依赖人工标注或合成数据的监督学习，性能受限于数据多样性与规模。本文提出一种无需调参的方法，复用预训练的扩散修复模型进行非可视分割。该方法基于修复模型的“无遮挡偏见”——修复后的物体通常为完整无遮挡形态。具体通过修复遮挡区域并直接进行分割，无需额外训练或微调。在五个数据集上的实验表明，该方法具备良好的泛化性与鲁棒性，平均比当前最优方法提升5.3%的掩码准确率。
原文摘要 · Abstract (English)
Amodal segmentation aims to predict segmentation masks for both the visible and occluded regions of an object. Most existing works formulate this as a supervised learning problem, requiring manually annotated amodal masks or synthetic training data. Consequently, their performance depends on the quality of the datasets, which often lack diversity and scale. This work introduces a tuning-free approach that repurposes pretrained diffusion-based inpainting models for amodal segmentation. Our approach is motivated by the "occlusion-free bias" of inpainting models, i.e., the inpainted objects tend to be complete objects without occlusions. Specifically, we reconstruct the occluded regions of an object via inpainting and then apply segmentation, all without additional training or fine-tuning. Experiments on five datasets demonstrate the generalizability and robustness of our approach. On average, our approach achieves 5.3% more accurate masks over the state-of-the-art.
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