发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出免训练一次性框架，用单张参考图结合DINOv3和特征正交化定位候选区域，经SAM分割，在显微镜和池沸腾数据集上平均IoU分别提升5.91%和78.62%。
AI 中文摘要
科学图像分割方法依赖大量标注和任务特定训练，限制了跨成像模态和实验条件的适应性。我们提出一个免训练、一次性的框架，利用单个标注参考图像来专门化视觉基础模型。该框架结合DINOv3表示与背景自适应特征正交化，以抑制与伪影相关的特征方向，随后余弦相似度定位候选区域供SAM分割。我们在红细胞显微镜、结构照明池沸腾和胸部X光摄影上评估该框架。相对于最强基线，所提方法在显微镜和池沸腾数据集上分别将平均IoU提高了5.91%和78.62%，同时在胸部X光片上达到相当性能。这些结果表明，一次性参考条件化可以使通用视觉模型适应专门的科学分割任务。
英文摘要
Scientific image segmentation methods rely on extensive annotation and task-specific training, limiting adaptation across imaging modalities and experimental conditions. We present a training-free, one-shot framework that specializes vision foundation models using a single annotated reference image. The framework combines DINOv3 representations with background-adaptive feature orthogonalization to suppress artifact-related feature directions, after which cosine similarity localizes candidate regions for SAM segmentation. We evaluate the framework on red-blood-cell microscopy, structured-illumination pool boiling, and chest radiography. Relative to the strongest baseline, the proposed method improves mean IoU by 5.91% and 78.62% on the microscopy and pool-boiling datasets, respectively, while achieving comparable performance on chest radiographs. These results demonstrate that one-shot reference conditioning can adapt general-purpose vision models to specialized scientific segmentation tasks.
