重新审视完全精度的必要性：面向错位建筑标签的弱监督目标级偏移校正
Revisiting the Necessity of Full Accuracy: Weakly Supervised Object-Level Offset Correction for Misaligned Building Labels
针对标注与影像存在空间错位时分割性能下降的问题，提供了一套可复用的两阶段流程：先用先验正则化的自对齐生成高置信度的目标级偏移伪标签，再训练实例级偏移回归模型来精修标签；可直接用于遥感建筑分割等存在标注错位的数据集构建与域自适应。
原文依据 OMAF first employs a prior-regularized self-alignment method to produce high-confidence, object-level offset pseudo-labels
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论Google Earth 影像结合建筑轮廓数据库，为构建本地化建筑数据集提供了一条高效途径。然而，这些影像缺乏正射校正，导致标注与其对应的屋顶位置之间存在空间错位。直接采用此类错位数据进行模型训练会严重降低分割性能。为应对这一挑战，我们提出一种基于对象的多阶段对齐框架（OMAF），该框架以极少的人工干预生成高质量的校正标签。OMAF 首先采用先验正则化的自对齐方法生成高置信度的目标级偏移伪标签，随后用这些伪标签训练实例级偏移回归模型以精修标签。在具有挑战性的数据集上的实验结果表明，OMAF 能有效校正错位，并使所有基线模型的平均交并比一致提升，最高达 40.6%。本工作为大规模数据集构建与域自适应提供了一种实用且低成本的解决方案。我们的代码与数据集已公开于 https://github.com/dayunyan/OMAF-Building-Alignment。
英文原文摘要 1168 字符
Google Earth imagery, combined with building footprint databases, offers an efficient way to construct localized building datasets. However, the lack of orthorectification in these images leads to spatial misalignments between annotations and their corresponding roof locations. Adopting such misaligned data directly for model training can severely degrade segmentation performance. To address the challenge, we propose an Object-based Multi-stage Alignment Framework (OMAF) that generates high-quality corrected labels with minimal manual intervention. OMAF first employs a prior-regularized self-alignment method to produce high-confidence, object-level offset pseudo-labels, which are then used to train an instance-level offset regression model for label refinement. Experimental results on the challenging datasets demonstrate that OMAF effectively corrects misalignments and consistently boosts the mIoU of all baseline models by up to 40.6%. This work provides a practical and cost-effective solution for large-scale dataset construction and domain adaptation. Our code and dataset are publicly available at https://github.com/dayunyan/OMAF-Building-Alignment.