Partial scan is a common approach to accelerate Magnetic Resonance Imaging
(MRI) data acquisition in both 2D and 3D settings. However, accurately
reconstructing images from partial scan data (i.e., incomplete k-space
matrices) remains challenging due to lack of an effectively global receptive
field in both spatial and k-space domains. To address this problem, we propose
the following: (1) a novel convolutional operator called Faster Fourier
Convolution (FasterFC) to replace the two consecutive convolution operations
typically used in convolutional neural networks (e.g., U-Net, ResNet). Based on
the spectral convolution theorem in Fourier theory, FasterFC employs
alternating kernels of size 1 in 3D case) in different domains to extend the
dual-domain receptive field to the global and achieves faster calculation speed
than traditional Fast Fourier Convolution (FFC). (2) A 2D accelerated MRI
method, FasterFC-End-to-End-VarNet, which uses FasterFC to improve the
sensitivity maps and reconstruction quality. (3) A multi-stage 3D accelerated
MRI method called FasterFC-based Single-to-group Network (FAS-Net) that
utilizes a single-to-group algorithm to guide k-space domain reconstruction,
followed by FasterFC-based cascaded convolutional neural networks to expand the
effective receptive field in the dual-domain. Experimental results on the
fastMRI and Stanford MRI Data datasets demonstrate that FasterFC improves the
quality of both 2D and 3D reconstruction. Moreover, FAS-Net, as a 3D
high-resolution multi-coil (eight) accelerated MRI method, achieves superior
reconstruction performance in both qualitative and quantitative results
compared with state-of-the-art 2D and 3D methods.

本文提出了一种基于 Faster Fourier Convolution 的方法，通过扩展全局接受域来更准确地重建部分扫描数据的 MRI 图像，实现了比传统方法更快速的计算速度和更高质量的重建效果。

利用更快的傅里叶卷积神经网络进行加速 MR 扫描的图像重建

Image Reconstruction for Accelerated MR Scan with Faster Fourier  Convolutional Neural Networks

We propose a novel convolutional operator for the task of point cloud
completion. One striking characteristic of our approach is that, conversely to
related work it does not require any max-pooling or voxelization operation.
Instead, the proposed operator used to learn the point cloud embedding in the
encoder extracts permutation-invariant features from the point cloud via a
soft-pooling of feature activations, which are able to preserve fine-grained
geometric details. These features are then passed on to a decoder architecture.
Due to the compression in the encoder, a typical limitation of this type of
architectures is that they tend to lose parts of the input shape structure. We
propose to overcome this limitation by using skip connections specifically
devised for point clouds, where links between corresponding layers in the
encoder and the decoder are established. As part of these connections, we
introduce a transformation matrix that projects the features from the encoder
to the decoder and vice-versa. The quantitative and qualitative results on the
task of object completion from partial scans on the ShapeNet dataset show that
incorporating our approach achieves state-of-the-art performance in shape
completion both at low and high resolutions.

我们提出了一种新的卷积算子，用于点云完成的任务。我们的方法的一个显著特点是与相关工作相反，它不需要任何最大池化或体素化操作。我们的算子不断提取具有置换不变性的特征，以保留细粒度的几何细节，通过对特征激活的软池化，从而学习编码器中的点云嵌入。通过特定设计的跳跃连接，将对应层之间的链接建立在编码器和解码器之间，以克服这类结构常见的限制，同时在点云完成任务中达到了最佳性能。

SoftPool++: 一种点云补全的编码解码网络

SoftPool++: An Encoder-Decoder Network for Point Cloud Completion

Face occlusions, covering either the majority or discriminative parts of the
face, can break facial perception and produce a drastic loss of information.
Biometric systems such as recent deep face recognition models are not immune to
obstructions or other objects covering parts of the face. While most of the
current face recognition methods are not optimized to handle occlusions, there
have been a few attempts to improve robustness directly in the training stage.
Unlike those, we propose to study the effect of generative face completion on
the recognition. We offer a face completion encoder-decoder, based on a
convolutional operator with a gating mechanism, trained with an ample set of
face occlusions. To systematically evaluate the impact of realistic occlusions
on recognition, we propose to play the occlusion game: we render 3D objects
onto different face parts, providing precious knowledge of what the impact is
of effectively removing those occlusions. Extensive experiments on the Labeled
Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that
face completion is able to partially restore face perception in machine vision
systems for improved recognition.

本文探究面部完整性对面部识别的影响，提出了一种基于卷积算子和门控机制的面部完整性编码器 - 解码器，并对真实的遮挡情况进行了分析和实验验证，证明了面部完整性可以部分恢复面部信息从而提高机器视觉系统的面部识别准确率。