POPoS：通过并行最优位置搜索改进高效且鲁棒的人脸关键点检测
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
AI总结：
本文提出并行最优位置搜索框架POPoS，通过伪距多边测量校正热图误差、多边测量锚点损失优化距离图及单步并行计算算法，在五个基准数据集上实现了高精度、高效率且鲁棒的人脸关键点检测，尤其在低分辨率热图场景下表现优异。
摘要：
在人脸关键点检测（FLD）中实现准确率与效率的平衡是一项关键挑战。本文提出了并行最优位置搜索（POPoS），这是一种高精度的编码-解码框架，旨在解决传统FLD方法的局限性。POPoS包含三个关键贡献：（1）利用伪距多边测量来校正热图误差，提高关键点定位精度。通过整合多个锚点，它减少了单个热图不准确性的影响，从而实现鲁棒的整体定位。（2）为了提高所选锚点的伪距精度，提出了一种名为多边测量锚点损失的新损失函数。该损失函数提高了距离图的准确性，减轻了局部最优的风险，并确保了最优解。（3）引入了一种单步并行计算算法，提升了计算效率并减少了处理时间。在五个基准数据集上的广泛评估表明，POPoS始终优于现有方法，特别是在低分辨率热图场景中表现出色，且计算开销极小。这些优势使POPoS成为FLD的高效且准确的工具，在实际场景中具有广泛的适用性。
英文摘要：
Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs three key contributions: (1) Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. By integrating multiple anchor points, it reduces the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time. Extensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution heatmaps scenarios with minimal computational overhead. These advantages make POPoS a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios.
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