Deep learning has made a huge impact on a wide variety of computer vision applications. But while the capabilities of deep neural networks are impressive, understanding how to best apply them is not straightforward. In this talk, we highlight key […]
Selecting the right imager and lens for your vision application is often a daunting challenge due to the vast number of products on the market and the large technical and performance differences between different product lines and technologies. In this […]
Commercial and industrial drones have the potential to completely disrupt industries and create new ones. Used in applications such as infrastructure inspection, search and rescue, package delivery, and many others, they can save time, money, and lives. Most of these […]
Camera systems used for computer vision at the edge are smarter than ever, but when they perceive the world in 2D, they remain limited for many applications because they lack information about the third dimension: depth. Sensing technologies that capture […]
Internet video streaming has recently experienced tremendous growth, but delivery quality remains critically dependent on network bandwidth. To mitigate bandwidth limitations, most video is compressed, resulting in image artifacts, noise, and blur. Quality is also degraded by image upscaling, which […]
We commonly train deep neural networks (DNNs) on existing data and then use the trained model to make predictions on new data. Once trained, these predictive models approximate a static mapping function from their input onto their predicted output. However, […]
Successful training of deep neural networks requires the right amounts and types of annotated training data. Collecting, curating and labeling this data is typically one of the most time-consuming aspects of developing a deep-learning-based solution. In this talk, we discuss […]
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding […]
In many modern vision and graphics applications, deep neural networks (DNNs) enable state-of-the-art performance for tasks like image classification, object detection and segmentation, quality enhancement and even new content generation. In this talk, we will demystify basic concepts behind DNN […]
In this presentation, we will provide an overview of workflows for deploying compressed deep learning models, starting with PyTorch and creating native C++ application code running in real-time on embedded hardware platforms. We illustrate these workflows on smartphones with real-world […]
