用框标注代替像素标注，实现高效少样本分割。
ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations
- 将背景建模为分布混合，无需训练即可完成分割。
- 在多个数据集上超越现有方法，性能显著提升。
- 适合机器人等真实场景中低资源标注需求。
在机器人应用中，少样本分割至关重要，可使机器人以极少训练数据完成复杂任务，适应多样真实环境。然而，即使少量图像的像素级标注也极为耗时且成本高昂。本文提出一种基于边界框标注的新型少样本二值分割方法。我们引入ProMi，一种基于原型混合的高效方法，将背景类视为分布混合体。该方法简单、无需训练、效果优异，能轻松处理粗略标注。相比现有基线，ProMi在不同数据集上均取得最佳结果，性能提升显著。此外，我们针对真实移动机器人任务设计了定性实验，验证了该方法的实际适用性。代码已开源：https://github.com/ThalesGroup/promi。
原文摘要 · Abstract (English)
In robotics applications, few-shot segmentation is crucial because it allows robots to perform complex tasks with minimal training data, facilitating their adaptation to diverse, real-world environments. However, pixel-level annotations of even small amount of images is highly time-consuming and costly. In this paper, we present a novel few-shot binary segmentation method based on bounding-box annotations instead of pixel-level labels. We introduce, ProMi, an efficient prototype-mixture-based method that treats the background class as a mixture of distributions. Our approach is simple, training-free, and effective, accommodating coarse annotations with ease. Compared to existing baselines, ProMi achieves the best results across different datasets with significant gains, demonstrating its effectiveness. Furthermore, we present qualitative experiments tailored to real-world mobile robot tasks, demonstrating the applicability of our approach in such scenarios. Our code: https://github.com/ThalesGroup/promi.
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