Deep learning (DL) models are capable of successfully exploiting latent
representations in MR data and have become state-of-the-art for accelerated MRI
reconstruction. However, undersampling the measurements in k-space as well as
the over- or under-parameterized and non-transparent nature of DL make these
models exposed to uncertainty. Consequently, uncertainty estimation has become
a major issue in DL MRI reconstruction. To estimate uncertainty, Monte Carlo
(MC) inference techniques have become a common practice where multiple
reconstructions are utilized to compute the variance in reconstruction as a
measurement of uncertainty. However, these methods demand high computational
costs as they require multiple inferences through the DL model. To this end, we
introduce a method to estimate uncertainty during MRI reconstruction using a
pixel classification framework. The proposed method, PixCUE (stands for Pixel
Classification Uncertainty Estimation) produces the reconstructed image along
with an uncertainty map during a single forward pass through the DL model. We
demonstrate that this approach generates uncertainty maps that highly correlate
with the reconstruction errors with respect to various MR imaging sequences and
under numerous adversarial conditions. We also show that the estimated
uncertainties are correlated to that of the conventional MC method. We further
provide an empirical relationship between the uncertainty estimations using
PixCUE and well-established reconstruction metrics such as NMSE, PSNR, and
SSIM. We conclude that PixCUE is capable of reliably estimating the uncertainty
in MRI reconstruction with a minimum additional computational cost.

提出一种名为 PixCUE 的方法，使用像素分类框架在单个向前传递中生成重建图像及不确定性地图以可靠地估计 MRI 重建过程中的不确定性，并与传统的 Monte Carlo 推断方法的结果相一致，同时产生与重建误差高度相关的不确定性地图。

PixCUE: MRI 中使用深度像素分类联合估计不确定性和图像重构

PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification

Semantic segmentation models are limited in their ability to scale to large
numbers of object classes. In this paper, we introduce the new task of
zero-shot semantic segmentation: learning pixel-wise classifiers for never-seen
object categories with zero training examples. To this end, we present a novel
architecture, ZS3Net, combining a deep visual segmentation model with an
approach to generate visual representations from semantic word embeddings. By
this way, ZS3Net addresses pixel classification tasks where both seen and
unseen categories are faced at test time (so called "generalized" zero-shot
classification). Performance is further improved by a self-training step that
relies on automatic pseudo-labeling of pixels from unseen classes. On the two
standard segmentation datasets, Pascal-VOC and Pascal-Context, we propose
zero-shot benchmarks and set competitive baselines. For complex scenes as ones
in the Pascal-Context dataset, we extend our approach by using a graph-context
encoding to fully leverage spatial context priors coming from class-wise
segmentation maps.

本文提出了一个新的任务：零样本语义分割，要求学习像素级分类器从未见过的物体类别中进行分类。ZS3Net 是本文提出的一种新架构，它将深度视觉分割模型与从语义词嵌入生成视觉表示的方法相结合，以实现在测试时间处理已见和未见类别的像素分类任务。本文还通过自我训练进一步提高了性能，并在 Pascal-VOC 和 Pascal-Context 两个标准分割数据集上提出了零样本基准并设立竞争基线。此外，对于像 Pascal-Context 数据集中的复杂场景，我们通过使用图形上下文编码来完全利用来自类别分割地图的空间上下文先验知识。

零样本语义分割

Zero-Shot Semantic Segmentation

"Background subtraction" is an old technique for finding moving objects in a
video sequence for example, cars driving on a freeway. The idea is that
subtracting the current image from a timeaveraged background image will leave
only nonstationary objects. It is, however, a crude approximation to the task
of classifying each pixel of the current image; it fails with slow-moving
objects and does not distinguish shadows from moving objects. The basic idea of
this paper is that we can classify each pixel using a model of how that pixel
looks when it is part of different classes. We learn a mixture-of-Gaussians
classification model for each pixel using an unsupervised technique- an
efficient, incremental version of EM. Unlike the standard image-averaging
approach, this automatically updates the mixture component for each class
according to likelihood of membership; hence slow-moving objects are handled
perfectly. Our approach also identifies and eliminates shadows much more
effectively than other techniques such as thresholding. Application of this
method as part of the Roadwatch traffic surveillance project is expected to
result in significant improvements in vehicle identification and tracking.

本文通过使用混合高斯模型和无监督的 EM 算法，提出了一种基于像素分类的背景减法方法，并应用于交通监控中，取得了较好的车辆识别和跟踪效果。