Different from handcrafted features, deep neural networks can automatically
learn task-specific features from data. Due to this data-driven nature, they
have achieved remarkable success in various areas. However, manual design and
selection of suitable network architectures are time-consuming and require
substantial effort of human experts. To address this problem, researchers have
proposed neural architecture search (NAS) algorithms which can automatically
generate network architectures but suffer from heavy computational cost and
instability if searching from scratch. In this paper, we propose a hybrid NAS
framework for ultrasound (US) image classification and segmentation. The hybrid
framework consists of a pre-trained backbone and several searched cells (i.e.,
network building blocks), which takes advantage of the strengths of both NAS
and the expert knowledge from existing convolutional neural networks.
Specifically, two effective and lightweight operations, a mixed depth-wise
convolution operator and a squeeze-and-excitation block, are introduced into
the candidate operations to enhance the variety and capacity of the searched
cells. These two operations not only decrease model parameters but also boost
network performance. Moreover, we propose a re-aggregation strategy for the
searched cells, aiming to further improve the performance for different vision
tasks. We tested our method on two large US image datasets, including a 9-class
echinococcosis dataset containing 9566 images for classification and an ovary
dataset containing 3204 images for segmentation. Ablation experiments and
comparison with other handcrafted or automatically searched architectures
demonstrate that our method can generate more powerful and lightweight models
for the above US image classification and segmentation tasks.

该论文提出了一种混合的神经架构搜索框架，将 NAS 算法与人类专家的先验知识相结合，使用混合的深度卷积操作和挤压 - 激励块来增强所搜索的网络单元的多样性和容量，通过消减模型参数和提高网络性能，达到更强大和轻量级的超声图像分类和分割模型。

HASA: 超声图像中包虫病分类与卵巢分割的混合架构搜索与聚合策略

HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images

In this work, we present the Densely Connected Temporal Convolutional Network
(DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional
Networks (TCN) have recently demonstrated great potential in many vision tasks,
its receptive fields are not dense enough to model the complex temporal
dynamics in lip-reading scenarios. To address this problem, we introduce dense
connections into the network to capture more robust temporal features.
Moreover, our approach utilises the Squeeze-and-Excitation block, a
light-weight attention mechanism, to further enhance the model's classification
power. Without bells and whistles, our DC-TCN method has achieved 88.36%
accuracy on the Lip Reading in the Wild (LRW) dataset and 43.65% on the
LRW-1000 dataset, which has surpassed all the baseline methods and is the new
state-of-the-art on both datasets.

本研究提出了一种名为 DC-TCN 的方法，使用密集连接的时间卷积网络和轻量级的 Squeeze-and-Excitation 注意力机制来进行单词的唇语识别，取得了 88.36％和 43.65％的识别准确率，超越了其他基准方法，成为两个数据集的新的最先进技术。

使用紧密连接的时间卷积网络进行唇语识别

Lip-reading with Densely Connected Temporal Convolutional Networks

Squeeze-and-Excitation (SE) block presents a channel attention mechanism for
modeling global context via explicitly capturing dependencies across channels.
However, we are still far from understanding how the SE block works. In this
work, we first revisit the SE block, and then present a detailed empirical
study of the relationship between global context and attention distribution,
based on which we propose a simple yet effective module, called Linear Context
Transform (LCT) block. We divide all channels into different groups and
normalize the globally aggregated context features within each channel group,
reducing the disturbance from irrelevant channels. Through linear transform of
the normalized context features, we model global context for each channel
independently. The LCT block is extremely lightweight and easy to be plugged
into different backbone models while with negligible parameters and
computational burden increase. Extensive experiments show that the LCT block
outperforms the SE block in image classification task on the ImageNet and
object detection/segmentation on the COCO dataset with different backbone
models. Moreover, LCT yields consistent performance gains over existing
state-of-the-art detection architectures, e.g., 1.5$\sim$1.7% AP$^{bbox}$ and
1.0$\sim$1.2% AP$^{mask}$ improvements on the COCO benchmark, irrespective of
different baseline models of varied capacities. We hope our simple yet
effective approach will shed some light on future research of attention-based
models.

本文提出 Linear Context Transform (LCT) 块，一种基于通道分组和线性变换的轻量级模块，可用于不同主干模型中。与 Squeeze-and-Excitation 块相比，LCT 块在 ImageNet 图像分类和 COCO 数据集目标检测 / 分割中，均获得了更好的性能，且对于不同基线模型均具有较高的一致性表现。

线性上下文转换块

Linear Context Transform Block

The central building block of convolutional neural networks (CNNs) is the
convolution operator, which enables networks to construct informative features
by fusing both spatial and channel-wise information within local receptive
fields at each layer. A broad range of prior research has investigated the
spatial component of this relationship, seeking to strengthen the
representational power of a CNN by enhancing the quality of spatial encodings
throughout its feature hierarchy. In this work, we focus instead on the channel
relationship and propose a novel architectural unit, which we term the
"Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise
feature responses by explicitly modelling interdependencies between channels.
We show that these blocks can be stacked together to form SENet architectures
that generalise extremely effectively across different datasets. We further
demonstrate that SE blocks bring significant improvements in performance for
existing state-of-the-art CNNs at slight additional computational cost.
Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017
classification submission which won first place and reduced the top-5 error to
2.251%, surpassing the winning entry of 2016 by a relative improvement of ~25%.
Models and code are available at this https URL

本研究提出了一种新的神经网络单元 SE 块来自适应地重新校准通道特征响应，进一步提高 CNN 架构的性能，并在 ILSVRC 2017 分类中取得了一流的成绩。