ICCV2023 | Vision Meets Drones: A Challenge
Vision Meets Drones:
A Challenge
无人机视觉挑战赛
After ECCV-VisDrone 2018, ICCV-VisDrone 2019, ECCV-VisDrone 2020, ICCV-VisDrone 2021, and PRCV- VisDrone 2022, VisDrone Challenge will be held on the ICCV 2023 workshop“Vision Meets Drones: A Challenge”(or VisDrone 2023) in Paris, France, for various core vision tasks on drone platform. We invite researchers to participate in the challenge and to evaluate and discuss their research at the workshop, as well as to submit papers describing research, experiments, or applications on drones.
ICCV-VisDrone 2023竞赛和研讨会将于2023年10月3日在法国巴黎举办。VisDrone2023挑战赛分为两个赛道，包括Object Detection Challenge（目标检测）以及Zero-Shot Detection Challenge（零样本目标检测）。本届比赛由天津大学、西北工业大学、浙江大学、香港科技大学、香港中文大学、中山大学以及澳大利亚国立大学共同承办，并获得了知名无人机公司一飞智控的赞助和支持。本届比赛鼓励各个参赛队伍使用大模型以及额外数据参赛，以探索空对地小目标检测性能的上限以及大模型的泛化能力。
CHALLENGE TASKS
挑战任务
1
Object Detection Challenge
目标检测
The task aims to detect objects of predefined categories (e.g., cars and pedestrians) from videos taken from drones.
该任务旨在从无人机拍摄的视频中检测预定义类别对象。
2
Zero-Shot Detection Challenge
零样本检测
The task aims to detect objects of unavailable categories during training from remote sensing images taken from drones or satellites.
该任务旨在从无人机或卫星拍摄的遥感图像中检测训练期间不可用的类别对象。
DETAILS
大赛细节
01
DATE 日期
[06.01]: Training, validation and testing data released
开放训练、验证和测试数据集
[06.15]: Evaluation software released
开放结果评估提交通道
[07.15]: Result submission deadline
各赛道截止提交
[10.03]: Challenge results released
公布比赛结果
[10.03]: Winner presents at ICCV 2023 Workshop
冠军在ICCV 2023研讨会上发表演讲
The deadline for the competition is 24:00 on July 15th 2023, GMT time
比赛截止时间2023年7月15日24:00 格林威治标准时间
02
AWARD 奖项
The top three from each track will receive a certificate.
The track winner will be awarded 10000RMB.
每个赛道的前三名将获得比赛证书，赛道冠军奖励10000RMB。
Champion will be invited to make a presentation.
The use of foundation model and additional dataset is also encouraged.
冠军将会受邀作报告，鼓励使用大模型和额外数据。
03
PARTICIPATION 参赛通道
website：http://aiskyeye.com/home/
Advisory Committee
咨询委员会
Qinghua Hu
胡清华
Professor
教授
Tianjin University
天津大学
Junwei Han
韩军伟
Professor
教授
Northwestern Polytechnical University
西北工业大学
Fatih Porikli
法提·波里克利
Professor
教授
Australian National University
澳大利亚国立大学
Organizing Committee
组委会
Pengfei Zhu
朱鹏飞
Associate Professor
副教授
Tianjin University
天津大学
Dingwen Zhang
张鼎文
Professor
教授
Northwestern Polytechnical University
西北工业大学
Wenguan Wang
王文冠
Professor
研究员
Zhejiang University
浙江大学
Zhijian He
何志坚
Research Associate
博士后
The Hong Kong University of Science and Technology
香港科技大学
Lei Xue
薛磊
Associate Professor
副教授
Sun Yat-Sen University
中山大学
Yixuan Yuan
袁奕萱
Assistant Professor
助理教授
Chinese University of Hong Kong
香港中文大学
Yu Wang
王煜
Associate Professor
副研究员
Tianjin University
天津大学
Bing Cao
曹兵
Associate Professor
副研究员
Tianjin University
天津大学
Xinjie Yao
姚鑫杰
Ph.D. Student
博士生
Tianjin University
天津大学
Laboratory
实验室介绍
The lab of Machine Learning and Data Mining at Tianjin University focuses on research in fundamental and applied aspects of machine learning, data mining, and pattern recognition, with a focus on the labeling, understanding, clustering, classification, and regression analysis of large-scale multi-source heterogeneous uncertain data. The lab has achieved significant research achievements in applications such as massive audio and video understanding, intelligent driving, intelligent unmanned systems, disaster space weather forecasting, and large equipment health monitoring. The lab has been selected as a Tianjin 131 Innovative Team and has been granted the Tianjin Key Laboratory of Machine Learning and the Engineering Research Center for Urban Intelligence and Digital Governance by the Ministry of Education. In recent years, the team has been funded by the National 973 Project, the National Key R&D Program, and the National Natural Science Foundation of China. They have published a series of papers in renowned international journals such as IEEE TPAMI, IJCV, IEEE TIP, IEEE TKDE, and international top conferences such as ICML, NeurIPS, ICLR, ICCV, and CVPR. They have also won several awards such as the Best Paper Award at the CCDM, CCF-AI, CCML, ICMLC, and ICME conferences. Members of the team have received support from various talent programs such as the National Natural Science Foundation of China's Distinguished Young Scholars Program, Outstanding Youth Science Fund, Youth Top-notch Talents Support Program, New Century Excellent Talents Support Program, Postdoctoral Innovation Talents Support Program, and the China Association for Science and Technology's Young Talent Support Program.
The laboratory is always welcoming scholars and students who are interested in conducting research on the fundamentals and applications of machine learning and pattern recognition. If you are interested, please send your resume to [email protected].
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