Ilya Krylov, Software Engineering Manager at Intel, presents the “Fast and Accurate RMNet: A New Neural Network for Embedded Vision” tutorial at the May 2019 Embedded Vision Summit. Usually, the top places in deep learning challenges are won by huge neural networks that require massive amounts of data and computation, making them impractical for use […]
Peter Vajda, Research Manager at Facebook, presents the “Hardware-aware Deep Neural Network Design” tutorial at the May 2019 Embedded Vision Summit. A central problem in the deployment of deep neural networks is maximizing accuracy within the compute performance constraints of embedded devices. In this talk, Vajda discusses approaches to addressing this challenge based on automated
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Oleg Sinyavskiy, Director of Research and Development at Brain Corp, presents the “Sensory Fusion for Scalable Indoor Navigation” tutorial at the May 2019 Embedded Vision Summit. Indoor autonomous navigation requires using a variety of sensors in different modalities. Merging together RGB, depth, lidar and odometry data streams to achieve autonomous operation requires a fusion of
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Mehrsan Javan, CTO of Sportlogiq, presents the “Teaching Machines to See, Understand, Describe and Predict Sports Games in Real Time” tutorial at the May 2019 Embedded Vision Summit. Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this means designing a fully-automated, robust, end-to-end pipeline; from visual input,
Nikita Manovich, Senior Software Engineer at Intel, presents the “Data Annotation at Scale: Pitfalls and Solutions” tutorial at the May 2019 Embedded Vision Summit. In many real-world use cases, deep learning algorithms work well if you have enough high-quality data to train them. Obtaining that data is a critical limiting factor in the development of
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Michael Gormish, Research Manager at Clarifai, presents the “Machine Learning- based Image Compression: Ready for Prime Time?” tutorial at the May 2019 Embedded Vision Summit. Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized
Frank Brill, Design Engineering Director at Cadence, presents the “Portable Performance via the OpenVX Computer Vision Library: Case Studies” tutorial at the May 2019 Embedded Vision Summit. OpenVX is a state-of-the-art open API standard for accelerating applications using computer vision and machine learning. The API and its conformance tests enable applications to leverage highly specialized
Adam Kraft, Deep Learning Engineer at Orbital Insight, presents the "Challenges and Approaches for Extracting Meaning from Satellite Imagery" tutorial at the May 2019 Embedded Vision Summit. Orbital Insight is a geospatial big data company leveraging the rapidly growing availability of satellite, UAV and other geospatial data sources to understand and characterize socioeconomic trends at
Ioannis Kakadiaris, Distinguished University Professor of Computer Science at the University of Houston, presents the "AI-powered Identity: Evaluating Face Recognition Capabilities" tutorial at the May 2019 Embedded Vision Summit. Following the deep learning renaissance, the face recognition community has achieved remarkable results when comparing images that are both frontal and non-occluded. However, significant challenges remain
Ilya Brailovskiy, Principal Engineer, and Changsoo Jeong, Head of Algorithm, both of Ring, present the "Optimizing SSD Object Detection for Low-power Devices" tutorial at the May 2019 Embedded Vision Summit. In this talk, Brailovskiy and Jeong discuss how Ring designs smart home video cameras to make neighborhoods safer. In particular, they focus on three key
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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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