Deep
Learning in Computer Vision
Computer vision tries to
acquire, process, analyze and understand visual data captured by all kinds of
sensors from the real world. It is a crossing discipline between computer
science and artificial intelligence, attracting a large population of
researchers all over the world. Recent advances in deep learning have provided
significant improvements in academia. As a result, deep learning has been
adopted in all kinds of computer vision tasks and many breakthroughs have
achieved in sub-areas, such as image classification, object detection and so
on.
This workshop mainly
focuses on deep learning in computer vision. We welcome original and
high-quality contributions from academia as well as industry on topics
including, but not limited to:
l Deep neural network
design for specific vision applications
l Deep learning for
feature representation
l Deep learning for video
surveillance
l Deep learning for object
recognition/retrieval
l Deep learning for scene
understanding
l Deep learning for face
analysis
l Deep learning for
activity recognition
l Deep learning for
biometrics
l Deep learning for remote
sensing
l Sparse coding in deep
learning
l Transfer learning for
deep learning
l Deep learning theory
l Deep learning for
dimension reduction
l Performance evaluation
of deep learning
l Optimization for deep
learning
Workshop
Chair:
Zhong
Zhang, Tianjin Normal University, zhong.zhang8848@gmail.com
Shuang
Liu, Tianjin Normal University, shuangliu.tjnu@gmail.com
Ronghua
Zhang, Shihezi University, zrh_oea@shzu.edu.cn
Workshop
Co-chair:
Baihua
Xiao, Institute of Automation, Chinese Academy of Sciences, baihua.xiao@ia.ac.cn
Liang
Zheng, Australian National University, liang.zheng@anu.edu.au
Yuan
Xie, East China Normal University, yxie@sei.ecnu.edu.cn
Yinglu
Liu, JD AI, liuyinglu1@jd.com
Lihu
Xiao, Beijing IrisKing Co., Ltd., xiaolh@irisking.com
Rui Jiang, Shanghai Maritime University, rjiang@shmtu.edu.cn
Smart
Air Traffic Management
Artificial Intelligence (AI) has
been applied to solve a variety of problems faced by air traffic management in
many countries. These include the need to increase the predictability of
traffic at different phases of flight, improve passenger flows at airports, and
enable greater automation of the system.
Smart air traffic management (ATM)
promotes operation efficiency, service quality, and management ability by
ultilizing AI technologies, e.g., natural language processing, neutral network,
and knowledge graph. Specifically, AI leading to automation would enable the
definition and application of smart strategies for managing air traffic and
ensuring a high degree of air-ground integration. Automation would also allow
for certain tasks to be offloaded, enabling pilots and air traffic controllers
to focus on safety critical tasks. Moreover, smart ATM uses the new generation
of information technology to facilitae extensive information sharing,
application, and coordination.
Solicited
topics include, but are not limited to:
l Instruction Recognition
and Processing
l ATM Knowledge
Representation and Acquisition
l System Wide Information
Management
l Intelligent Decision
Support System
l Big Data in ATM
l Smart Airport
l Airport Collaborative
Decision Making
l Application of AI in ATM
l Decision Making based on
Weather Information
Workshop
Chair:
Mao
Yi, State Key Laboratory of Air Traffic Management System and Technology, mao_y@nuaa.edu.cn