The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant
of the Residual Networks (ResNets) by stacking a small number of residual
blocks with large channel sizes, have demonstrated outstanding performance on
multiple dense prediction tasks. However, since proposed, the Wide-ResNet
architecture has barely evolved over the years. In this work, we revisit its
architecture design for the recent challenging panoptic segmentation task,
which aims to unify semantic segmentation and instance segmentation. A baseline
model is obtained by incorporating the simple and effective
Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets.
Its network capacity is further scaled up or down by adjusting the width (i.e.,
channel size) and depth (i.e., number of layers), resulting in a family of
SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such
a simple scaling scheme, coupled with grid search, identifies several
SWideRNets that significantly advance state-of-the-art performance on panoptic
segmentation datasets in both the fast model regime and strong model regime.

本文重新设计 Wide-ResNets 的架构用于最新的全景分割任务，通过简单且有效的 Squeeze-and-Excitation 和 Switchable Atrous Convolution 结合调整网络的宽度和深度，形成了一系列 SWideRNets 并通过网格搜索得到了多种在快速和强大模型方案下都可以显著提高全景分割数据集状态的 SWideRNets。

用于全景分割的宽残差网络的扩展

Scaling Wide Residual Networks for Panoptic Segmentation

Over the past decade, multivariate time series classification has received
great attention. We propose transforming the existing univariate time series
classification models, the Long Short Term Memory Fully Convolutional Network
(LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series
classification model by augmenting the fully convolutional block with a
squeeze-and-excitation block to further improve accuracy. Our proposed models
outperform most state-of-the-art models while requiring minimum preprocessing.
The proposed models work efficiently on various complex multivariate time
series classification tasks such as activity recognition or action recognition.
Furthermore, the proposed models are highly efficient at test time and small
enough to deploy on memory constrained systems.

本研究将现有的单变量时间序列分类模型 LSTM-FCN 和 ALSTM-FCN 转化为多变量时间序列分类模型，通过增加 squeeze-and-excitation 块改善了分类的准确性。提出的模型适用于各种复杂的多变量时间序列分类任务，如活动识别或动作识别。此外，提出的模型在测试时非常高效，且足够小以部署在内存受限的系统上。

用于时间序列分类的多元 LSTM-FCNs

Multivariate LSTM-FCNs for Time Series Classification

In this paper, we present a simple and parameter-efficient drop-in module for
one-stage object detectors like SSD when learning from scratch (i.e., without
pre-trained models). We call our module GFR (Gated Feature Reuse), which
exhibits two main advantages. First, we introduce a novel gate-controlled
prediction strategy enabled by Squeeze-and-Excitation to adaptively enhance or
attenuate supervision at different scales based on the input object size. As a
result, our model is more effective in detecting diverse sizes of objects.
Second, we propose a feature-pyramids structure to squeeze rich spatial and
semantic features into a single prediction layer, which strengthens feature
representation and reduces the number of parameters to learn. We apply the
proposed structure on DSOD and SSD detection frameworks, and evaluate the
performance on PASCAL VOC 2007, 2012 and COCO datasets. With fewer model
parameters, GFR-DSOD outperforms the baseline DSOD by 1.4%, 1.1%, 1.7% and
0.6%, respectively. GFR-SSD also outperforms the original SSD and SSD with
dense prediction by 3.6% and 2.8% on VOC 2007 dataset. Code is available at:
this https URL .

本文提出了一种针对 SSD 等单阶段目标检测器的简单参数高效的插入式模块，称为 GFR（门控特征复用），该模块通过 Squeeze-and-Excitation 引入了一种新的门控预测策略来自适应地增强或减弱不同比例的目标，并提出了一个特征金字塔结构来将丰富的空间和语义特征压缩到单个预测层中，使特征更强，参数更少。