用少量粗标注指导SAM，实现高效精准的物体部件分割。
Guided SAM: Label-Efficient Part Segmentation
- 从易获取的粗粒度块标注中学习位置提示，引导SAM聚焦目标部件。
- 在汽车部件数据集上，平均交并比从0.37提升至0.49。
- 仅需五分之一标注成本，适合标注资源有限的场景。
精确定位物体部件对物体识别和机器人操作等任务至关重要。现有部件分割方法依赖大量训练数据和人工标注。分割一切模型（SAM）在多种分割任务中表现良好，但需手动提供位置提示以指导分割区域；且因其训练对象为完整物体而非部件，易导致部件过度分割。为此，我们提出一种新方法，通过粗粒度块标注学习位置提示，引导SAM聚焦相关部件。该方法在图像块上训练分类器以识别部件类别，并将块聚合为感兴趣区域（ROIs）及对应位置提示。利用这些提示与区域，对SAM进行条件化处理，从而显著提升效率并减少人工标注。实验表明，在汽车部件数据集上，该方法使平均交并比从0.37提升至0.49，标注成本降低约五倍。
原文摘要 · Abstract (English)
Localizing object parts precisely is essential for tasks such as object recognition and robotic manipulation. Recent part segmentation methods require extensive training data and labor-intensive annotations. Segment-Anything Model (SAM) has demonstrated good performance on a wide range of segmentation problems, but requires (manual) positional prompts to guide it where to segment. Furthermore, since it has been trained on full objects instead of object parts, it is prone to over-segmentation of parts. To address this, we propose a novel approach that guides SAM towards the relevant object parts. Our method learns positional prompts from coarse patch annotations that are easier and cheaper to acquire. We train classifiers on image patches to identify part classes and aggregate patches into regions of interest (ROIs) with positional prompts. SAM is conditioned on these ROIs and prompts. This approach, termed `Guided SAM', enhances efficiency and reduces manual effort, allowing effective part segmentation with minimal labeled data. We demonstrate the efficacy of Guided SAM on a dataset of car parts, improving the average IoU on state of the art models from 0.37 to 0.49 with annotations that are on average five times more efficient to acquire.
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