In this talk, we will explore the use of multimodal large language models in real-world edge applications. We will begin by explaining how these large multimodal models (LMMs) work and highlighting their key components, giving special attention to how LMMs […]
In this talk, we will take an in-depth look at the rapidly advancing and fast-growing space of driver assistance and autonomous driving. We will explore how ADAS systems are adding new capabilities each year, and how image sensors and processors […]
This talk delves into the challenges and optimization strategies in image signal processing (ISP) for enhancing object detection in advanced driver-assistance systems (ADAS). Through real-world examples, we explore the critical role of image tuning in addressing corner cases and improving […]
Over the past three decades, the convergence of machine learning, big data and enhanced computing power has transformed the field of computer vision from basic image and signal processing to complex perception. The recent decade, marked by the emergence of […]
In this presentation we will delve into an innovative application of computer vision for power utility infrastructure inspection. We’ll begin with an overview of Buzz Solutions’ technology, emphasizing the use of computer vision for addressing critical challenges faced by power […]
In the fast-paced world of embedded vision applications, moving rapidly from concept to deployment is crucial. In this presentation, Dan Mihai Dumitriu introduces the Edge App Runtime and SDK—groundbreaking tools designed to streamline and accelerate the development process for edge […]
In this presentation, we will introduce the essential features of cameras for embedded vision applications. We will explain lens mounts and camera interface options such as MIPI, USB and GigE. We’ll explore sensor features and trade-offs, including rolling shutter vs. […]
In most AI research today, deep neural networks (DNNs) are designed solely to improve prediction accuracy, often ignoring real-world constraints such as compute and memory requirements. Embedded vision developers typically prefer to use these state-of-the-art (SOTA) DNNs from the research […]
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including […]
Transformers are a class of neural network models originally designed for natural language processing. Transformers have also proven to be powerful for visual perception due to their exceptional ability to model long-range dependencies in images and process multimodal data. Resource […]
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