Abstract
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.
Abstract (translated)
非模态分割的目标是为物体的可见和被遮挡区域预测分割掩码。大多数现有的方法将这个问题定义为一个监督学习问题，需要手动标注非模态掩码或合成训练数据。因此，它们的表现依赖于数据集的质量，而这些数据集往往缺乏多样性和规模。这项工作提出了一种无需调优的方法，该方法重新利用了预先训练的基于扩散的修复模型来进行非模态分割。我们的方法受到“无遮挡偏置”（即修复的对象倾向于是完整的、没有被遮挡的对象）这一特性启发。具体来说，我们通过修复来重建物体的遮挡区域，并在此基础上进行分割，整个过程无需额外的训练或微调。在五个数据集上的实验表明了我们的方法具有良好的泛化能力和鲁棒性。平均而言，与现有最佳方法相比，我们的方法实现了5.3%更准确的掩码效果。
URL
https://arxiv.org/abs/2503.18947
