REBASE：基于参考-背景子空间消除的免训练上下文分割

摘要

训练无关的上下文分割能够在推理时仅凭一张带标注的参考图像引入新目标类别，从而消除了类增量学习中的重新训练和内存开销。现有方法通过将视觉基础模型（用于语义对应）与可提示分割网络（如SAM）相结合来实现这一目标。然而，其性能从根本上受到跨图像相似度图质量的限制：参考图像与查询图像之间共享的上下文背景会系统性地提升非目标区域的相似度，从而降低提示定位的准确性。本文提出REBASE，一个训练无关的框架，显式抑制这些虚假的上下文对应关系。我们的方法从参考图像中识别出低秩背景特征子空间，并将参考和查询特征投影到该子空间的正交补上（以闭式求解），从而得到更干净的语义匹配。随后，我们利用基于相似度加权的远点采样生成正点提示，并结合细化的稠密相似度先验。无需任何训练或参数更新，我们的方法在PACO-Part、FSS-1000以及跨域数据集（如ISIC2018）上均达到了训练无关方法的最新性能，表明显式去除背景子空间是一种非常有效的单样本定位原则。

English

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.