On October 23, 2024 at 9 am PT (noon ET), the Edge AI and Vision Alliance will deliver the free symposium “Your Next Computer Vision Model Might be an LLM: Generative AI and the Move From Large Language Models to Vision Language Models.” Here’s the description, from the event registration page: The past decade has […]
Free Online Symposium Explores LLMs and VLMs for Computer Vision Read More +
Yong Jae Lee, Associate Professor in the Department of Computer Sciences at the University of Wisconsin-Madison and CEO of GivernyAI, presents the “Learning to Understand Our Multimodal World with Minimal Supervision” tutorial at the May 2024 Embedded Vision Summit. The field of computer vision is undergoing another profound change. Recently, “generalist” models have emerged that
Nikita Tiwari, AI Enabling Engineer for OEM PC Experiences in the Client Computing Group at Intel, presents the “Identifying and Mitigating Bias in AI” tutorial at the May 2024 Embedded Vision Summit. From autonomous driving to immersive shopping, and from enhanced video collaboration to graphic design, AI is placing a wealth of possibilities at our
“Identifying and Mitigating Bias in AI,” a Presentation from Intel Read More +
Amit Mate, Founder and CEO of GMAC Intelligence, presents the “Fundamentals of Training AI Models for Computer Vision Applications” tutorial at the May 2024 Embedded Vision Summit. In this presentation, Mate introduces the essential aspects of training convolutional neural networks (CNNs). He discusses the prerequisites for training, including models, data and training frameworks, with an
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. A Snapdragon Insider chats with Qualcomm Technologies’ Said Bakadir about the future of smart glasses and Qualcomm Technologies’ role in turning it into a critical AI tool Artificial intelligence (AI) is increasingly winding its way through our
Snapdragon Powers the Future of AI in Smart Glasses. Here’s How Read More +
Sébastien Taylor, Vice President of Research and Development for Au-Zone Technologies, presents the “Introduction to Semantic Segmentation” tutorial at the May 2024 Embedded Vision Summit. Vision applications often rely on object detectors, which determine the nature and location of objects in a scene. But many vision applications require a different type of visual understanding: semantic
“An Introduction to Semantic Segmentation,” a Presentation from Au-Zone Technologies Read More +
On September 10, 2024 at 9:00 am PT (noon ET), Alliance Member companies Ceva and Visionary.ai will deliver the free webinar “Can AI Make Cameras See In the Dark? Real-Time Video Enhancement.” From the event page: As cameras become ubiquitous in applications such as surveillance, mobile, drones, and automotive systems, achieving clear vision 24/7 under
Upcoming Webinar Explores How AI Can Make Cameras See In the Dark Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. NVIDIA has announced the latest v0.15 release of NVIDIA TensorRT Model Optimizer, a state-of-the-art quantization toolkit of model optimization techniques including quantization, sparsity, and pruning. These techniques reduce model complexity and enable downstream inference frameworks like NVIDIA
Dwith Chenna, Member of the Technical Staff and Product Engineer for AI Inference at AMD, presents the “DNN Quantization: Theory to Practice” tutorial at the May 2024 Embedded Vision Summit. Deep neural networks, widely used in computer vision tasks, require substantial computation and memory resources, making it challenging to run these models on resource-constrained devices.
“DNN Quantization: Theory to Practice,” a Presentation from AMD Read More +
Hiram Rayo Torres Rodriguez, Senior AI Research Engineer at NXP Semiconductors, presents the “Leveraging Neural Architecture Search for Efficient Computer Vision on the Edge” tutorial at the May 2024 Embedded Vision Summit. In most AI research today, deep neural networks (DNNs) are designed solely to improve prediction accuracy, often ignoring real-world constraints such as compute
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