Facial landmark detection is an essential technology for driver status
tracking and has been in demand for real-time estimations. As a landmark
coordinate prediction, heatmap-based methods are known to achieve a high
accuracy, and Lite-HRNet can achieve a fast estimation. However, with
Lite-HRNet, the problem of a heavy computational cost of the fusion block,
which connects feature maps with different resolutions, has yet to be solved.
In addition, the strong output module used in HRNetV2 is not applied to
Lite-HRNet. Given these problems, we propose a novel architecture called
Lite-HRNet Plus. Lite-HRNet Plus achieves two improvements: a novel fusion
block based on a channel attention and a novel output module with less
computational intensity using multi-resolution feature maps. Through
experiments conducted on two facial landmark datasets, we confirmed that
Lite-HRNet Plus further improved the accuracy in comparison with conventional
methods, and achieved a state-of-the-art accuracy with a computational
complexity with the range of 10M FLOPs.

通过实验证明，Lite-HRNet Plus 在人脸标志物检测领域取得了比传统方法更高的准确性，并在 10M FLOPs 的计算复杂性范围内实现了最新的准确性。

Lite-HRNet Plus：快速准确的面部关键点检测

Lite-HRNet Plus: Fast and Accurate Facial Landmark Detection

Heatmap-based methods have become the mainstream method for pose estimation
due to their superior performance. However, heatmap-based approaches suffer
from significant quantization errors with downscale heatmaps, which result in
limited performance and the detrimental effects of intermediate supervision.
Previous heatmap-based methods relied heavily on additional post-processing to
mitigate quantization errors. Some heatmap-based approaches improve the
resolution of feature maps by using multiple costly upsampling layers to
improve localization precision. To solve the above issues, we creatively view
the backbone network as a degradation process and thus reformulate the heatmap
prediction as a Super-Resolution (SR) task. We first propose the SR head, which
predicts heatmaps with a spatial resolution higher than the input feature maps
(or even consistent with the input image) by super-resolution, to effectively
reduce the quantization error and the dependence on further post-processing.
Besides, we propose SRPose to gradually recover the HR heatmaps from LR
heatmaps and degraded features in a coarse-to-fine manner. To reduce the
training difficulty of HR heatmaps, SRPose applies SR heads to supervise the
intermediate features in each stage. In addition, the SR head is a lightweight
and generic head that applies to top-down and bottom-up methods. Extensive
experiments on the COCO, MPII, and CrowdPose datasets show that SRPose
outperforms the corresponding heatmap-based approaches. The code and models are
available at this https URL

基于热图的方法已经成为姿势估计的主流方法，但由于通过降低重要热图的大小而带来量化误差的原因，这些方法受到了一定的限制，而且中间监督也起到了有害效果。为了解决以上问题，该论文提出了 SRPose 方法，通过将骨干网络视为降级过程并重新构建热图预测作为超分辨率任务，从而有效降低了量化误差和进一步后处理的依赖。实验结果显示 SRPose 在 COCO、MPII 和 CrowdPose 数据集上的性能优于其他基于热图的方法。

人体姿态估计轻量级超分辨率头

Lightweight Super-Resolution Head for Human Pose Estimation

In the field of human pose estimation, regression-based methods have been
dominated in terms of speed, while heatmap-based methods are far ahead in terms
of performance. How to take advantage of both schemes remains a challenging
problem. In this paper, we propose a novel human pose estimation framework
termed DistilPose, which bridges the gaps between heatmap-based and
regression-based methods. Specifically, DistilPose maximizes the transfer of
knowledge from the teacher model (heatmap-based) to the student model
(regression-based) through Token-distilling Encoder (TDE) and Simulated
Heatmaps. TDE aligns the feature spaces of heatmap-based and regression-based
models by introducing tokenization, while Simulated Heatmaps transfer explicit
guidance (distribution and confidence) from teacher heatmaps into student
models. Extensive experiments show that the proposed DistilPose can
significantly improve the performance of the regression-based models while
maintaining efficiency. Specifically, on the MSCOCO validation dataset,
DistilPose-S obtains 71.6% mAP with 5.36M parameter, 2.38 GFLOPs and 40.2 FPS,
which saves 12.95x, 7.16x computational cost and is 4.9x faster than its
teacher model with only 0.9 points performance drop. Furthermore, DistilPose-L
obtains 74.4% mAP on MSCOCO validation dataset, achieving a new
state-of-the-art among predominant regression-based models.

本文提出了一种新的人体姿态估计框架 DistilPose，它通过 Token-distilling Encoder 和 Simulated Heatmaps 将基于 heatmap 的和基于回归的方法结合起来，在保持有效性的同时显著提高了回归模型的性能。