Recently, deep convolutional neural network (CNN) have been widely used in
image restoration and obtained great success. However, most of existing methods
are limited to local receptive field and equal treatment of different types of
information. Besides, existing methods always use a multi-supervised method to
aggregate different feature maps, which can not effectively aggregate
hierarchical feature information. To address these issues, we propose an
attention cube network (A-CubeNet) for image restoration for more powerful
feature expression and feature correlation learning. Specifically, we design a
novel attention mechanism from three dimensions, namely spatial dimension,
channel-wise dimension and hierarchical dimension. The adaptive spatial
attention branch (ASAB) and the adaptive channel attention branch (ACAB)
constitute the adaptive dual attention module (ADAM), which can capture the
long-range spatial and channel-wise contextual information to expand the
receptive field and distinguish different types of information for more
effective feature representations. Furthermore, the adaptive hierarchical
attention module (AHAM) can capture the long-range hierarchical contextual
information to flexibly aggregate different feature maps by weights depending
on the global context. The ADAM and AHAM cooperate to form an "attention in
attention" structure, which means AHAM's inputs are enhanced by ASAB and ACAB.
Experiments demonstrate the superiority of our method over state-of-the-art
image restoration methods in both quantitative comparison and visual analysis.
Code is available at this https URL

本文提出了一种基于注意力机制的深度卷积神经网络用于图像修复，在空间、通道和层次三个维度加入注意力机制以用于更好的特征表达和特征相关性学习。实验表明该方法在定量和视觉分析上优于现有方法。

基于注意力立方体网络的图像恢复

Attention Cube Network for Image Restoration

Most existing semantic segmentation methods employ atrous convolution to
enlarge the receptive field of filters, but neglect partial information. To
tackle this issue, we firstly propose a novel Kronecker convolution which
adopts Kronecker product to expand the standard convolutional kernel for taking
into account the partial feature neglected by atrous convolutions. Therefore,
it can capture partial information and enlarge the receptive field of filters
simultaneously without introducing extra parameters. Secondly, we propose
Tree-structured Feature Aggregation (TFA) module which follows a recursive rule
to expand and forms a hierarchical structure. Thus, it can naturally learn
representations of multi-scale objects and encode hierarchical contextual
information in complex scenes. Finally, we design Tree-structured Kronecker
Convolutional Networks (TKCN) which employs Kronecker convolution and TFA
module. Extensive experiments on three datasets, PASCAL VOC 2012,
PASCAL-Context and Cityscapes, verify the effectiveness of our proposed
approach. We make the code and the trained model publicly available at
this https URL

本文提出了基于 Kronecker 卷积和树形特征聚合 (TFA) 模块的树形 Kronecker 卷积网络 (TKCN)，旨在解决现有的语义分割方法在扩大接受区域时忽略局部信息的问题，能够同时捕获局部信息和扩大滤波器的接收域，进一步实现多尺度物体的表示学习和复杂场景中的分层上下文信息编码。

基于 Kronecker 的树状卷积网络用于语义分割

Tree-structured Kronecker Convolutional Network for Semantic  Segmentation

Weakly supervised temporal action localization, which aims at temporally
locating action instances in untrimmed videos using only video-level class
labels during training, is an important yet challenging problem in video
analysis. Many current methods adopt the "localization by classification"
framework: first do video classification, then locate temporal area
contributing to the results most. However, this framework fails to locate the
entire action instances and gives little consideration to the local context. In
this paper, we present a novel architecture called Cascaded Pyramid Mining
Network (CPMN) to address these issues using two effective modules. First, to
discover the entire temporal interval of specific action, we design a two-stage
cascaded module with proposed Online Adversarial Erasing (OAE) mechanism, where
new and complementary regions are mined through feeding the erased feature maps
of discovered regions back to the system. Second, to exploit hierarchical
contextual information in videos and reduce missing detections, we design a
pyramid module which produces a scale-invariant attention map through combining
the feature maps from different levels. Final, we aggregate the results of two
modules to perform action localization via locating high score areas in
temporal Class Activation Sequence (CAS). Extensive experiments conducted on
THUMOS14 and ActivityNet-1.3 datasets demonstrate the effectiveness of our
method.

本文介绍了一种名为级联金字塔挖掘网络的新型体系结构，旨在通过两个有效模块解决弱监督的时间动作定位问题，并在 THUMOS14 和 ActivityNet-1.3 数据集上进行了广泛的实验和验证。