Panoptic segmentation is a cutting-edge computer vision technique that bridges the gap between object detection and scene segmentation, providing a holistic understanding of an image. By simultaneously identifying individual objects (instance segmentation) and classifying entire regions (semantic segmentation), panoptic segmentation […]

Deep learning has transformed many aspects of computer vision, including object detection, enabling accurate and efficient identification of objects in images and videos. However, choosing the right deep neural network-based object detector for your project, particularly when deploying on lightweight […]

Activity detection and recognition are crucial tasks in various industries, including surveillance and sports analytics. In this talk, we’ll provide an in-depth exploration of human activity understanding, covering the fundamentals of activity detection and recognition, and the challenges of individual […]

In this session we’ll explain two neural network quantization techniques, quantization-aware training (QAT) and post-training quantization (PTQ), and explain when to use each. We’ll discuss what needs to be done for efficient implementation of each: for example, QAT requires preparation […]

In this presentation we introduce radar for machine perception. Radar is a proven technology with a long history of successive improvements that plays an increasingly important role in robust perception systems. We’ll delve into the fundamental principles of non-imaging and […]

This talk provides a high-level introduction to artificial intelligence and deep learning, covering the basics of machine learning and the key concepts of deep learning. We will explore the different types of deep learning architectures, including fully connected networks, convolutional […]

As edge computing demands smaller, more efficient models, knowledge distillation emerges as a key approach to model compression. We delve into the details of this process, exploring what knowledge distillation entails and the requirements for its implementation, including dataset size […]

Transformer neural networks have revolutionized artificial intelligence by introducing an architecture built around self-attention mechanisms. This has enabled unprecedented advances in understanding sequential data, such as human languages, while also dramatically improving accuracy on nonsequential tasks like object detection. In […]