Atrous convolutions are employed as a method to increase the receptive field
in semantic segmentation tasks. However, in previous works of semantic
segmentation, it was rarely employed in the shallow layers of the model. We
revisit the design of atrous convolutions in modern convolutional neural
networks (CNNs), and demonstrate that the concept of using large kernels to
apply atrous convolutions could be a more powerful paradigm. We propose three
guidelines to apply atrous convolutions more efficiently. Following these
guidelines, we propose DSNet, a Dual-Branch CNN architecture, which
incorporates atrous convolutions in the shallow layers of the model
architecture, as well as pretraining the nearly entire encoder on ImageNet to
achieve better performance. To demonstrate the effectiveness of our approach,
our models achieve a new state-of-the-art trade-off between accuracy and speed
on ADE20K, Cityscapes and BDD datasets. Specifically, DSNet achieves 40.0% mIOU
with inference speed of 179.2 FPS on ADE20K, and 80.4% mIOU with speed of 81.9
FPS on Cityscapes. Source code and models are available at Github:
this https URL

在语义分割任务中，采用空洞卷积作为增加感受野的方法，本研究通过在模型浅层引入大核心空洞卷积以及预训练图像编码器等策略，提出了 DSNet，一种新的双分支卷积神经网络结构，它在 ADE20K、Cityscapes 和 BDD 数据集上以更高的准确率和速度获得了最新的表现。

DSNet：一种在语义分割中使用空洞卷积的新方法

DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation

One persistent challenge in deep learning based speech emotion recognition
(SER) is the unconscious encoding of emotion-irrelevant factors (e.g., speaker
or phonetic variability), which limits the generalization of SER in practical
use. In this paper, we propose DSNet, a Disentangled Siamese Network with
neutral calibration, to meet the demand for a more robust and explainable SER
model. Specifically, we introduce an orthogonal feature disentanglement module
to explicitly project the high-level representation into two distinct
subspaces. Later, we propose a novel neutral calibration mechanism to encourage
one subspace to capture sufficient emotion-irrelevant information. In this way,
the other one can better isolate and emphasize the emotion-relevant information
within speech signals. Experimental results on two popular benchmark datasets
demonstrate the superiority of DSNet over various state-of-the-art methods for
speaker-independent SER.

通过引入 DSNet，一个具有中立校准的解缠结孪生网络，以满足更强大和可解释的语音情感识别模型的需求，并在两个流行的基准数据集上的实验结果证明其优越性。

DSNet: 融合中性校准的分解孪生网络用于语音情感识别

DSNet: Disentangled Siamese Network with Neutral Calibration for Speech  Emotion Recognition

Crowd counting has been widely studied by computer vision community in recent
years. Due to the large scale variation, it remains to be a challenging task.
Previous methods adopt either multi-column CNN or single-column CNN with
multiple branches to deal with this problem. However, restricted by the number
of columns or branches, these methods can only capture a few different scales
and have limited capability. In this paper, we propose a simple but effective
network called DSNet for crowd counting, which can be easily trained in an
end-to-end fashion. The key component of our network is the dense dilated
convolution block, in which each dilation layer is densely connected with the
others to preserve information from continuously varied scales. The dilation
rates in dilation layers are carefully selected to prevent the block from
gridding artifacts. To further enlarge the range of scales covered by the
network, we cascade three blocks and link them with dense residual connections.
We also introduce a novel multi-scale density level consistency loss for
performance improvement. To evaluate our method, we compare it with
state-of-the-art algorithms on four crowd counting datasets (ShanghaiTech,
UCF-QNRF, UCF_CC_50 and UCSD). Experimental results demonstrate that DSNet can
achieve the best performance and make significant improvements on all the four
datasets (30% on the UCF-QNRF and UCF_CC_50, and 20% on the others).

本文提出了一种名为 DSNet 的网络，采用密集空洞卷积块和密集残差连接来实现完全端到端的人群计数。在四个人群计数数据集上对 DSNet 进行实验后，得到了比现有算法更优秀的结果。

密集尺度网络用于人群计数

Dense Scale Network for Crowd Counting

In recent years, Discriminative Correlation Filter (DCF) based tracking
methods have achieved great success in visual tracking. However, the
multi-resolution convolutional feature maps trained from other tasks like image
classification, cannot be naturally used in the conventional DCF formulation.
Furthermore, these high-dimensional feature maps significantly increase the
tracking complexity and thus limit the tracking speed. In this paper, we
present a deep and shallow feature learning network, namely DSNet, to learn the
multi-level same-resolution compressed (MSC) features for efficient online
tracking, in an end-to-end offline manner. Specifically, the proposed DSNet
compresses multi-level convolutional features to uniform spatial resolution
features. The learned MSC features effectively encode both appearance and
semantic information of objects in the same-resolution feature maps, thus
enabling an elegant combination of the MSC features with any DCF-based methods.
Additionally, a channel reliability measurement (CRM) method is presented to
further refine the learned MSC features. We demonstrate the effectiveness of
the MSC features learned from the proposed DSNet on two DCF tracking
frameworks: the basic DCF framework and the continuous convolution operator
framework. Extensive experiments show that the learned MSC features have the
appealing advantage of allowing the equipped DCF-based tracking methods to
perform favorably against the state-of-the-art methods while running at high
frame rates.

本研究提出了一种深浅特征学习网络 DSNet，通过 MSC 特征的学习，利用任何基于 DCF 的方法进行有效的在线追踪，同时可显著提高跟踪速度。