骨骼不能是三角形：通过协同误差修正实现准确高效的椎骨关键点估计
Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision
AI总结：
针对交互式椎骨关键点估计依赖用户纠错的问题，提出 KeyBot，通过识别典型误差并用模拟误差训练来自动协同修正，在三个公开数据集上达到最先进性能并降低用户负担。
摘要：
交互式关键点估计方法的最新进展在提高准确性的同时，最大限度地减少了用户干预。然而，这些方法需要用户输入来修正误差；在不准确关键点密集聚集或重叠的椎骨关键点估计中，这可能代价高昂。我们提出了一种名为 KeyBot 的新方法，专门用于识别和修正现有模型中的显著且典型的误差，类似于用户修正。通过刻画典型误差类型并使用模拟误差进行训练，KeyBot 可有效修正这些误差，并显著减少用户工作量。在三个公开数据集上进行的全面定量和定性评估证实，KeyBot 显著优于现有方法，在交互式椎骨关键点估计中取得了最先进性能。源代码和演示视频可在 https://ts-kim.github.io/KeyBot/ 获取。
英文摘要：
Recent advances in interactive keypoint estimation methods have enhanced accuracy while minimizing user intervention. However, these methods require user input for error correction, which can be costly in vertebrae keypoint estimation where inaccurate keypoints are densely clustered or overlap. We introduce a novel approach, KeyBot, specifically designed to identify and correct significant and typical errors in existing models, akin to user revision. By characterizing typical error types and using simulated errors for training, KeyBot effectively corrects these errors and significantly reduces user workload. Comprehensive quantitative and qualitative evaluations on three public datasets confirm that KeyBot significantly outperforms existing methods, achieving state-of-the-art performance in interactive vertebrae keypoint estimation. The source code and demo video are available at: https://ts-kim.github.io/KeyBot/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
