Our goal is to design architectures that retain the groundbreaking
performance of Convolutional Neural Networks (CNNs) for landmark localization
and at the same time are lightweight, compact and suitable for applications
with limited computational resources. To this end, we make the following
contributions: (a) we are the first to study the effect of neural network
binarization on localization tasks, namely human pose estimation and face
alignment. We exhaustively evaluate various design choices, identify
performance bottlenecks, and more importantly propose multiple orthogonal ways
to boost performance. (b) Based on our analysis, we propose a novel
hierarchical, parallel and multi-scale residual architecture that yields large
performance improvement over the standard bottleneck block while having the
same number of parameters, thus bridging the gap between the original network
and its binarized counterpart. (c) We perform a large number of ablation
studies that shed light on the properties and the performance of the proposed
block. (d) We present results for experiments on the most challenging datasets
for human pose estimation and face alignment, reporting in many cases
state-of-the-art performance. (e) We further provide additional results for the
problem of facial part segmentation. Code can be downloaded from
this https URL

本文提出了一种具有轻量、紧凑和适用于适用于计算资源有限应用的体系结构，并在人体姿势估计和面部对齐等定位任务上，详细研究了神经网络二值化的效果，提出了多种正交方法来提高性能，在此基础上，提出了一种新的分层、并行和多尺度残差架构，同时具有与标准瓶颈块相同数量的参数，从而填补了原始网络与其二值化对应物之间的差距，大幅提高了性能，并在人体姿势估计和面部对齐等最具挑战性的数据集上获得了最前沿的表现，同时探究了其性质与性能，并支持关于面部部分分割的其他结果。

利用有限资源的分层二元卷积神经网络进行关键点定位

Hierarchical binary CNNs for landmark localization with limited  resources

Facial alignment involves finding a set of landmark points on an image with a
known semantic meaning. However, this semantic meaning of landmark points is
often lost in 2D approaches where landmarks are either moved to visible
boundaries or ignored as the pose of the face changes. In order to extract
consistent alignment points across large poses, the 3D structure of the face
must be considered in the alignment step. However, extracting a 3D structure
from a single 2D image usually requires alignment in the first place. We
present our novel approach to simultaneously extract the 3D shape of the face
and the semantically consistent 2D alignment through a 3D Spatial Transformer
Network (3DSTN) to model both the camera projection matrix and the warping
parameters of a 3D model. By utilizing a generic 3D model and a Thin Plate
Spline (TPS) warping function, we are able to generate subject specific 3D
shapes without the need for a large 3D shape basis. In addition, our proposed
network can be trained in an end-to-end framework on entirely synthetic data
from the 300W-LP dataset. Unlike other 3D methods, our approach only requires
one pass through the network resulting in a faster than real-time alignment.
Evaluations of our model on the Annotated Facial Landmarks in the Wild (AFLW)
and AFLW2000-3D datasets show our method achieves state-of-the-art performance
over other 3D approaches to alignment.

通过使用三维空间变形网络（3DSTN）模型相机投影矩阵和三维模型的形变参数，我们提出了一种新颖的方法同时提取面部的三维形状和语义一致的二维对齐，可以在比实时更快的速度下通过网络一次传递实现。我们的模型在 AFLW 和 AFLW2000-3D 数据集上进行评估，证明了我们的方法在三维对齐方面取得了最先进的性能。