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Embedded Vision Summit 2021

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May 25, 2021 - May 27, 2021
Virtual
Edge AI and Vision Alliance
1 (925) 954-1411

About this Event
For innovators adding computer vision and AI to products.

The Summit attracts a global audience of companies developing vision and edge AI-enabled products including embedded systems, cloud solutions and mobile applications.

For the second consecutive year, the Summit is available as a 100% online experience; both live and on-demand!

Why Attend? A First-Rate Program with Powerful Insights into Practical Visual AI

Join us for hundreds of hours of learning; from workshops to the latest technical insights, business trends and vision technologies; all with a focus on practical, deployable computer vision and visual AI. The Summit connects the theories from great academic conferences, like CVPR, to reality.

4 Major Reasons the Embedded Vision Summit is Different from the Rest

  • The Summit is by innovators, for innovators.
  • We have a relentless focus on practical information for people incorporating vision and AI into products to solve real-world problems
  • We've been doing this for 10 years.
  • A whopping 98% of our attendees would recommend the Summit to a colleague.

  • Call for Papers
    Abstracts, Feb. 3
    If you are interested in having a session at the Summit, send your proposal as soon as possible. The program is being formed now! Session Topics will continue to include a wide range of topics related to practical computer vision and visual AI applications. We especially welcome sessions on the following topics: Top-to-bottom software stack for sensor-based AI systems and applications, Tools and techniques for training and deployment of deep neural networks, Using other types of sensors for AI (radar, lidar, speech/audio, etc.), in conjunction with or as alternatives to image sensors, Depth sensing, Tracking, Assessing algorithm accuracy in real-world applications, Privacy, security and bias in sensor-based AI systems, Selecting processors, image sensors, algorithms and development tools, Low-power deep learning, Edge-cloud trade-offs, Federated and edge learning, Obtaining and managing training data (including synthetic data and data augmentation), Enterprise applications, Applications in mobile autonomous machines (cars, robots, drones, etc.), Applications in healthcare (medical imaging, home healthcare, etc.), Applications in smart spaces (retail analytics, security, etc.)


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