论文标题
级别：学习通用功能，并进行排名姿势估计的等级监督
RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation
论文作者
论文摘要
我们解决了基于RGB图像的头部姿势估计的具有挑战性的问题。我们首先将头部姿势表示为将其限制为有限的空间。头姿势表示为矢量投影或向量角度显示有助于提高性能。此外，提出了排名损失与MSE回归损失相结合的。排名损失通过同一个人的配对样本来监督神经网络，并惩罚姿势预测的错误排序。对这种新损失功能的分析表明，它有助于更好的局部特征提取器，其中特征将其推广到抽象地标，这些地标是与姿势相关的特征，而不是姿势 - 意外信息，例如身份，年龄和照明。广泛的实验表明，我们的方法在公共数据集上的当前最新方案大大优于：AFLW2000和BIWI。我们的模型可在AFLW2000上的先前Sota Mae和BIWI上的显着改善，分别从4.50到3.66，分别从4.0到3.71。源代码将在以下网址提供：https：//github.com/seathiefwang/rankheadpose。
We address the challenging problem of RGB image-based head pose estimation. We first reformulate head pose representation learning to constrain it to a bounded space. Head pose represented as vector projection or vector angles shows helpful to improving performance. Further, a ranking loss combined with MSE regression loss is proposed. The ranking loss supervises a neural network with paired samples of the same person and penalises incorrect ordering of pose prediction. Analysis on this new loss function suggests it contributes to a better local feature extractor, where features are generalised to Abstract Landmarks which are pose-related features instead of pose-irrelevant information such as identity, age, and lighting. Extensive experiments show that our method significantly outperforms the current state-of-the-art schemes on public datasets: AFLW2000 and BIWI. Our model achieves significant improvements over previous SOTA MAE on AFLW2000 and BIWI from 4.50 to 3.66 and from 4.0 to 3.71 respectively. Source code will be made available at: https://github.com/seathiefwang/RankHeadPose.
