Image segmentation is a critical enabler for tasks ranging from medical
diagnostics to autonomous driving. However, the correct segmentation semantics
- where are boundaries located? what segments are logically similar? - change
depending on the domain, such that state-of-the-art foundation models can
generate meaningless and incorrect results. Moreover, in certain domains,
fine-tuning and retraining techniques are infeasible: obtaining labels is
costly and time-consuming; domain images (micrographs) can be exponentially
diverse; and data sharing (for third-party retraining) is restricted. To enable
rapid adaptation of the best segmentation technology, we propose the concept of
semantic boosting: given a zero-shot foundation model, guide its segmentation
and adjust results to match domain expectations. We apply semantic boosting to
the Segment Anything Model (SAM) to obtain microstructure segmentation for
transmission electron microscopy. Our booster, SAM-I-Am, extracts geometric and
textural features of various intermediate masks to perform mask removal and
mask merging operations. We demonstrate a zero-shot performance increase of
(absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06%
drop in mean false positive masks across images of three difficulty classes
over vanilla SAM (ViT-L).

给定零样本分割技术基础模型，我们提出了 “语义增强” 的概念，通过指导分割过程并调整结果以适应特定领域的期望，实现了最佳分割技术的快速适应。我们将语义增强应用于 Segment Anything Model（SAM）以获取透射电子显微镜的微观结构分割，通过提取各种中间掩模的几何和纹理特征进行掩模去除和掩模合并操作，结果在三个难度类别的图像上相对于原始的 SAM（ViT-L）分别获得了平均 IoU 增加 21.35%、12.6%、5.27% 和平均假阳性掩模降低 9.91%、18.42%、4.06%。