In this technical report, we present our solution to the Traffic4Cast 2021 Core Challenge, in which participants were asked to develop algorithms for predicting a traffic state 60 minutes ahead, based on the information from the previous hour, in 4 different cities. In contrast to the previously held competitions, this year's challenge focuses on the temporal domain shift in traffic due to the COVID-19 pandemic. Following the past success of U-Net, we utilize it for predicting future traffic maps. Additionally, we explore the usage of pre-trained encoders such as DenseNet and EfficientNet and employ multiple domain adaptation techniques to fight the domain shift. Our solution has ranked third in the final competition. The code is available at https://github.com/jbr-ai-labs/traffic4cast-2021.
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Boston University（波士顿大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Georgetown University（乔治城大学）；MIT-IBM Watson Lab（麻省理工学院-IBM沃森实验室）；Hanbat National University（韩巴国立大学）；SI Analytics（SI分析公司）；Tel-Aviv University（特拉维夫大学）；Beijing Institute of Technology（北京理工大学）
PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges
Multimedia Laboratory, The Chinese University of Hong Kong（香港中文大学多媒体实验室）；The University of Hong Kong（香港大学）
在这份技术报告中，我们展示了Waymo Open Dataset Challenges 2020中针对3D检测、3D跟踪和领域自适应三个赛道的最佳LiDAR解决方案。我们的竞赛解决方案是建立在我们最近提出的PV-RCNN 3D目标检测框架之上的。我们的PV-RCNN的几个变种进行了探索，包括时间信息纳入，动态体素化，自适应训练样本选择，分类与ROI功能，等一个简单的模型集成策略，采用非最大值抑制和投票箱，以产生最终的结果。通过仅使用LiDAR点云数据，我们的模型最终在Waymo Open Dataset Challenges的3D检测，3D跟踪和领域自适应三个赛道上获得了所有LiDAR方法中的第一名，以及所有多模态方法中的第二名。我们的解决方案将在https://github.com/open-mmlab/OpenPCDet上提供
Importance Weighted Adversarial Nets for Partial Domain Adaptation
Advanced Multimedia Research Lab, University of Wollongong, Australia（伍伦贡大学高级多媒体研究实验室，澳大利亚伍伦贡大学高级多媒体研究实验室（注：此处“Advanced Multimedia Research Lab”是实验室，属于University of Wollongong的机构，整体按要求提取到大学级别，即University of Wollongong，澳大利亚伍伦贡大学））
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
Queen Mary University of London（伦敦玛丽女王大学）；National Technical University of Athens（雅典国家技术大学）；National Infrastructures for Research and Technology（国家研究与技术基础设施机构）
