CVPR 2018 Is the Premier Conference and Expo focused on Machine Learning, Artificial Intelligence, and Computer Vision where applications include Autonomous Driving and Facial Recognition

Last year more than 5,000 attendees gathered and visited 120 exhibitors who featured the newest hardware systems, software, and career opportunities, and this year we expect that number to grow.

CVPR 2018 will include the industrys leading professionals and students pursuing employment in every aspect of the computer vision and artificial intelligence - and exhibit space is going fast!


Last year more than 5,000 attendees gathered and visited 120 exhibitors who featured the newest hardware systems, software, and career opportunities, and this year we expect that number to grow. Technology leaders, students, academics, and industry researchers will be looking for originations that are active in the industry.

Here is what organizations that participated as an exhibitor and sponsor indicated about their participation in 2017:
• 91% rated the effectiveness of their participation Excellent/Good
• 81% rated the quantity of traffic to their booth space Excellent/Good
• 83% indicated they are planning to increase or maintain their exhibit presence for 2018

Organizations that have already reserved exhibit space include:
• NVIDIA • Google
• Facebook • AutoX
• Adobe Systems • Microsoft
• Mapiliary • Qualcomm
• Tencent • Amazon
• Intel • Disney Research
• Cadence Design Systems • and many more!


IEEE/CVF Computer Vision and Pattern Recognition Conference
Conference Dates: June 18 - 23, 2018 - Expo Dates: June 19 - 21, 2018
Salt Palace Convention Center • Salt Lake City, Utah

Featured Product

PI USA - High Precision R-Theta Stages for Semicon, Laser Processing Apps

PI USA - High Precision R-Theta Stages for Semicon, Laser Processing Apps

2-axis R-Theta motion systems are better suited to spiral motion than Cartesian XY stages. R-Theta systems often find applications in precision laser / semiconductor applications due to higher throughput, precision, and smaller size. See examples and options.