Glass-like objects can be seen everywhere in our daily life which are very
hard for existing methods to segment them. The properties of transparencies
pose great challenges of detecting them from the chaotic background and the
vague separation boundaries further impede the acquisition of their exact
contours. Moving machines which ignore glasses have great risks of crashing
into transparent barriers or difficulties in analysing objects reflected in the
mirror, thus it is of substantial significance to accurately locate glass-like
objects and completely figure out their contours. In this paper, inspired by
the scale integration strategy and the refinement method, we proposed a
brand-new network, named as MGNet, which consists of a Fine-Rescaling and
Merging module (FRM) to improve the ability to extract spatially relationship
and a Primary Prediction Guiding module (PPG) to better mine the leftover
semantics from the fused features. Moreover, we supervise the model with a
novel loss function with the uncertainty-aware loss to produce high-confidence
segmentation maps. Unlike the existing glass segmentation models that must be
trained on different settings with respect to varied datasets, our model are
trained under consistent settings and has achieved superior performance on
three popular public datasets. Code is available at

借鉴比例整合策略和改良方法，我们提出了一种全新的名为 MGNet 的网络，它包括一个 Fine-Rescaling 和 Merging 模块（FRM）来提高提取空间关系的能力，一个 Primary Prediction Guiding 模块（PPG）来更好地从融合后的特征中挖掘剩余语义，并且我们使用一种新的带有不确定性感知损失的损失函数来监督模型生成高置信度的分割地图。与现有的必须在不同设置下训练的玻璃分割模型不同，我们的模型在统一设置下训练，并在三个流行的公共数据集上取得了卓越的性能。可在指定链接获取代码。

多尺度与主要预测指导的玻璃分割

Glass Segmentation with Multi Scales and Primary Prediction Guiding

By investigating iterative methods for a constrained linear model, we propose
a new class of fully connected V-cycle MgNet for long-term time series
forecasting, which is one of the most difficult tasks in forecasting. MgNet is
a CNN model that was proposed for image classification based on the multigrid
(MG) methods for solving discretized partial differential equations (PDEs). We
replace the convolutional operations with fully connected operations in the
existing MgNet and then apply them to forecasting problems. Motivated by the
V-cycle structure in MG, we further propose the FV-MgNet, a V-cycle version of
the fully connected MgNet, to extract features hierarchically. By evaluating
the performance of FV-MgNet on popular data sets and comparing it with
state-of-the-art models, we show that the FV-MgNet achieves better results with
less memory usage and faster inference speed. In addition, we develop ablation
experiments to demonstrate that the structure of FV-MgNet is the best choice
among the many variants.

该论文提出了一种新型的全连接 V-cycle MgNet，它是用于长期时间序列预测的迭代方法，并取得了比现有最先进模型更好的预测结果。

FV-MgNet: 可解释时间序列预测的全连接 V 循环 MgNet

FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting

We introduce MGNet, a multi-task framework for monocular geometric scene
understanding. We define monocular geometric scene understanding as the
combination of two known tasks: Panoptic segmentation and self-supervised
monocular depth estimation. Panoptic segmentation captures the full scene not
only semantically, but also on an instance basis. Self-supervised monocular
depth estimation uses geometric constraints derived from the camera measurement
model in order to measure depth from monocular video sequences only. To the
best of our knowledge, we are the first to propose the combination of these two
tasks in one single model. Our model is designed with focus on low latency to
provide fast inference in real-time on a single consumer-grade GPU. During
deployment, our model produces dense 3D point clouds with instance aware
semantic labels from single high-resolution camera images. We evaluate our
model on two popular autonomous driving benchmarks, i.e., Cityscapes and KITTI,
and show competitive performance among other real-time capable methods. Source
code is available at this https URL

MGNet 是一个用于单目几何场景理解的多任务框架，结合了全景分割和自监督单目深度估计两个任务，旨在提供低延迟、实时单 GPU 推理，实现单高分辨率摄像头图像的密集 3D 点云和实例感知语义标签。研究人员在 Cityscapes 和 KITTI 数据集上进行了验证，发现其与其他实时方法的性能相当。

MGNet: 自动驾驶的单目几何场景理解

MGNet: Monocular Geometric Scene Understanding for Autonomous Driving

We develop a unified model, known as MgNet, that simultaneously recovers some
convolutional neural networks (CNN) for image classification and multigrid (MG)
methods for solving discretized partial differential equations (PDEs). This
model is based on close connections that we have observed and uncovered between
the CNN and MG methodologies. For example, pooling operation and feature
extraction in CNN correspond directly to restriction operation and iterative
smoothers in MG, respectively. As the solution space is often the dual of the
data space in PDEs, the analogous concept of feature space and data space
(which are dual to each other) is introduced in CNN. With such connections and
new concept in the unified model, the function of various convolution
operations and pooling used in CNN can be better understood. As a result,
modified CNN models (with fewer weights and hyper parameters) are developed
that exhibit competitive and sometimes better performance in comparison with
existing CNN models when applied to both CIFAR-10 and CIFAR-100 data sets.

MgNet 模型同时恢复了用于图像分类的卷积神经网络 (CNN) 和求解离散化偏微分方程 (PDE) 的多重网格 (MG) 方法，该模型基于 CNN 和 MG 方法之间的密切联系，介绍了特征空间和数据空间的对偶概念，最终实现了修改后具有更好性能的 CNN 模型。