This paper presents the baseline approach for the organized 2nd Covid-19
Competition, occurring in the framework of the AIMIA Workshop in the European
Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database
which is annotated for COVID-19 detction, consisting of about 7,700 3-D CT
scans. Part of the database consisting of Covid-19 cases is further annotated
in terms of four Covid-19 severity conditions. We have split the database and
the latter part of it in training, validation and test datasets. The former two
datasets are used for training and validation of machine learning models, while
the latter will be used for evaluation of the developed models. The baseline
approach consists of a deep learning approach, based on a CNN-RNN network and
report its performance on the COVID19-CT-DB database.

本文介绍在欧洲计算机视觉会议（ECCV 2022）AIMIA 研讨会框架下组织的第二次 COVID-19 竞赛的基线方法，并介绍了 COV19-CT-DB 数据库和基于 CNN-RNN 网络的深度学习方法的性能表现。

AI-MIA: 应用医学影像进行 COVID-19 的检测和严重程度分析

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist
medical specialists in vital circumstances. Deep learning methodologies
constitute a main approach for chest CT scan analysis and disease prediction.
However, large annotated databases are necessary for developing deep learning
models that are able to provide COVID-19 diagnosis across various medical
environments in different countries. Due to privacy issues, publicly available
COVID-19 CT datasets are highly difficult to obtain, which hinders the research
and development of AI-enabled diagnosis methods of COVID-19 based on CT scans.
In this paper we present the COV19-CT-DB database which is annotated for
COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in
training, validation and test datasets. The former two datasets can be used for
training and validation of machine learning models, while the latter will be
used for evaluation of the developed models. We also present a deep learning
approach, based on a CNN-RNN network and report its performance on the
COVID19-CT-DB database.

本文介绍了一个包含约 5,000 个三维 CT 扫描的标注 COVID-19 的数据库（COV19-CT-DB），其中分为训练集、验证集和测试集，并且提供了基于 CNN-RNN 网络的深度学习方法以及其在该数据库上的性能报告。

MIA-COV19D: 基于三维胸部 CT 图像分析的 COVID-19 检测

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Learning the spatial-temporal representation of motion information is crucial
to human action recognition. Nevertheless, most of the existing features or
descriptors cannot capture motion information effectively, especially for
long-term motion. To address this problem, this paper proposes a long-term
motion descriptor called sequential Deep Trajectory Descriptor (sDTD).
Specifically, we project dense trajectories into two-dimensional planes, and
subsequently a CNN-RNN network is employed to learn an effective representation
for long-term motion. Unlike the popular two-stream ConvNets, the sDTD stream
is introduced into a three-stream framework so as to identify actions from a
video sequence. Consequently, this three-stream framework can simultaneously
capture static spatial features, short-term motion and long-term motion in the
video. Extensive experiments were conducted on three challenging datasets: KTH,
HMDB51 and UCF101. Experimental results show that our method achieves
state-of-the-art performance on the KTH and UCF101 datasets, and is comparable
to the state-of-the-art methods on the HMDB51 dataset.

本文提出了一种名为连续深层轨迹描述符（sDTD）的长期运动描述符，它通过将稠密轨迹投影到二维平面上，再使用 CNN-RNN 网络来学习长期运动的有效表示，进而将 sDTD 流引入三流框架以从视频序列中识别动作。实验证明该方法在 KTH 和 UCF101 数据集上取得了最先进的性能，且在 HMDB51 数据集上与最先进的方法相媲美。