The point cloud learning community witnesses a modeling shift from CNNs to
Transformers, where pure Transformer architectures have achieved top accuracy
on the major learning benchmarks. However, existing point Transformers are
computationally expensive since they need to generate a large attention map,
which has quadratic complexity (both in space and time) with respect to input
size. To solve this shortcoming, we introduce Patch ATtention (PAT) to
adaptively learn a much smaller set of bases upon which the attention maps are
computed. By a weighted summation upon these bases, PAT not only captures the
global shape context but also achieves linear complexity to input size. In
addition, we propose a lightweight Multi-Scale aTtention (MST) block to build
attentions among features of different scales, providing the model with
multi-scale features. Equipped with the PAT and MST, we construct our neural
architecture called PatchFormer that integrates both modules into a joint
framework for point cloud learning. Extensive experiments demonstrate that our
network achieves comparable accuracy on general point cloud learning tasks with
9.2x speed-up than previous point Transformers.

在点云学习领域中，由于 Transformer 模型生成的注意力矩阵具有输入规模的平方复杂度，因此本文提出了一种采用自适应方式学习较小基础上的基础并通过它们的带权求和来计算注意力矩阵的方法 (PAT)，并借助于轻量级的多尺度注意力块（MST）来实现对不同尺度特征的关注，最终构建了一个名为 PatchFormer 的神经网络，为点云学习提供了可比较的准确性，并且比以前的点 Transformers 快 9.2 倍。

PatchFormer：一种具有补丁注意力的高效点变换器

PatchFormer: An Efficient Point Transformer with Patch Attention

Facial landmarks (FLM) estimation is a critical component in many
face-related applications. In this work, we aim to optimize for both accuracy
and speed and explore the trade-off between them. Our key observation is that
not all faces are created equal. Frontal faces with neutral expressions
converge faster than faces with extreme poses or expressions. To differentiate
among samples, we train our model to predict the regression error after each
iteration. If the current iteration is accurate enough, we stop iterating,
saving redundant iterations while keeping the accuracy in check. We also
observe that as neighboring patches overlap, we can infer all facial landmarks
(FLMs) with only a small number of patches without a major accuracy sacrifice.
Architecturally, we offer a multi-scale, patch-based, lightweight feature
extractor with a fine-grained local patch attention module, which computes a
patch weighting according to the information in the patch itself and enhances
the expressive power of the patch features. We analyze the patch attention data
to infer where the model is attending when regressing facial landmarks and
compare it to face attention in humans. Our model runs in real-time on a mobile
device GPU, with 95 Mega Multiply-Add (MMA) operations, outperforming all
state-of-the-art methods under 1000 MMA, with a normalized mean error of 8.16
on the 300W challenging dataset.

研究了面部关键点估计的准确性和速度问题，提出了一种基于软件的面部关键点姿态估计方法，利用逐步迭代预测回归误差的方法来区分样本，通过多尺度、基于补丁的轻量级特征提取器来进行面部关键点姿态估计，在移动设备 GPU 上实现了实时运行。