REBASE：基于参考背景子空间消除的无需训练的上下文内分割方法
REBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation
摘要
无需训练的上下文分割技术使得在推理过程中能够从单个标注参考图像中引入新的物体类别，从而避免了类增量学习所需的重新训练和内存消耗。近期的方法通过将视觉基础模型与可提示分割网络（如SAM）结合来实现这一目标。然而，它们的性能本质上受到跨图像相似性映射质量的影响；参考图像与查询图像之间的共享上下文背景会系统性地提高非目标区域的相似性，进而降低提示的定位精度。我们提出了REBASE框架，该框架无需训练即可有效消除这些虚假的上下文对应关系。我们的方法能够从参考图像中识别出低秩背景特征子空间，并将参考图像和查询图像的特征投影到其正交补空间中，从而实现更清晰的语义匹配。随后，我们采用基于相似度加权的最远点采样方法生成正提示，同时结合更精细的密集相似性先验。无需任何训练或参数更新，我们的方法在PACO-Part、FSS-1000以及ISIC2018等跨领域数据集上实现了无需训练方法中的最新水平，表明明确去除背景特征子空间是一种非常有效的单样本定位方法。
English Abstract
Training-free in-context segmentation enables new object categories to be introduced at inference time from a single annotated reference image, eliminating the retraining and memory overhead of class-incremental learning. Recent approaches achieve this by combining vision foundation models for semantic correspondence with promptable segmentation networks like SAM. However, their performance is fundamentally limited by the quality of the cross-image similarity map; shared contextual backgrounds between the reference and query systematically elevate similarity in non-target regions, degrading prompt localization. We present REBASE, a training-free framework that explicitly suppresses these spurious contextual correspondences. Our method identifies the low-rank background feature subspace from the reference image and project the reference and query features onto its orthogonal complement in closed form, yielding cleaner semantic matching. We then generate positive point prompts using similarity-weighted farthest-point sampling, paired with a refined dense similarity prior. Without any training or parameter updates, our approach establishes a new state of the art among training-free methods on PACO-Part, FSS-1000, and cross-domain datasets such as ISIC2018, demonstrating that explicit background subspace removal is a highly effective principle for one-shot localization.