KeyPosS：通过受GPS启发的真距多点定位实现即插即用的人脸关键点检测
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
AI总结：
本文提出KeyPosS框架，首次将GPS真距多点定位引入人脸关键点检测，通过全卷积网络预测锚点并三角化距离以精确推导关键点坐标，实现即插即用集成，在低分辨率下以最小计算开销取得最先进性能。
摘要：
精确的人脸关键点检测对于人脸分析任务至关重要，然而主流的热力图和坐标回归方法面临计算成本过高和量化误差的问题。通过全面的理论分析和实验，我们识别并阐明了现有技术的局限性。为克服这些挑战，我们率先将最初为GPS定位设计的真距多点定位（True-Range Multilateration）应用于人脸关键点检测。我们提出了关键点定位系统（KeyPosS）——这是第一个通过三角化兴趣点与由全卷积网络预测的锚点之间的距离来推导精确关键点坐标的框架。KeyPosS的一个关键优势是其即插即用特性，能够灵活集成到多种解码流程中。在四个数据集上的大量实验证明了最先进的性能，KeyPosS在低分辨率设置下以最小的计算开销超越了现有方法。通过率先将多点定位与人脸分析相结合，KeyPosS标志着人脸关键点检测的范式转变。代码可在 https://github.com/zhiqic/KeyPosS 获取。
英文摘要：
Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and quantization errors. Through comprehensive theoretical analysis and experimentation, we identify and elucidate the limitations of existing techniques. To overcome these challenges, we pioneer the application of True-Range Multilateration, originally devised for GPS localization, to facial landmark detection. We propose KeyPoint Positioning System (KeyPosS) - the first framework to deduce exact landmark coordinates by triangulating distances between points of interest and anchor points predicted by a fully convolutional network. A key advantage of KeyPosS is its plug-and-play nature, enabling flexible integration into diverse decoding pipelines. Extensive experiments on four datasets demonstrate state-of-the-art performance, with KeyPosS outperforming existing methods in low-resolution settings despite minimal computational overhead. By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection. The code is available at https://github.com/zhiqic/KeyPosS.
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。
