Fourier Feature Networks are an exciting new development in Computer Vision, and their use for modeling radiance fields has produced a range of impressive results at the meeting point of Computer Vision and Computer Graphics.…
In this talk, we will offer an entirely white-box interpretation of deep (convolutional) networks from the perspective of data compression and group invariance. We’ll show how modern deep-layered architectures, linear (convolutional) operators and nonlinear activations,…
Big data-driven deep learning has helped significantly improve the performance of visual tasks in the past few years, but it has also exhibited limitations in scalability and adaptation to real-world scenarios. Researchers and practitioners are…
Deep learning plus huge training data is a popular paradigm in computer vision. However, after a decade of growth, it’s time to revisit its strengths and weaknesses. Will there be a new trend in computer…
Big data-driven deep learning has helped significantly improve the performance of visual tasks in the past few years, but it has also exhibited limitations in scalability and adaptation to real-world scenarios. Researchers and practitioners are…
The limitations of big data-driven deep learning in scalability and adaptation to real-world scenarios hinder its practical applications. To address these limitations, it’s extremely important to develop architectures and algorithms that can capture the fundamentals…
Speakers: Hanwang Zhang, Professor, Nanyang Technological University Yuwang Wang, Senior Researcher, Microsoft Research Asia Shujian Yu, Professor, UiT – The Arctic University of Norway One of the critical shortcomings of big data-driven deep learning is…
As the Gaming and Entertainment industry has grown to encompass a global consumer base, digital content creators face mounting challenges in scaling to meet consumer demand. A new opportunity is emerging to provide artists, designers,…
Speaker: Katsushi Ikeuchi, Sr. Principal Research Manager, Microsoft Research Redmond Learning-from-Observation (LfO), a robot-teaching paradigm, aims to build a robot system that understands what humans do through a small number of human observations and map…
Humans learn, infer, and reason by leveraging prior knowledge without necessarily observing a large number of examples. Visual learning and reasoning technologies, such as few-shot and zero-shot learning, aim to enable human-like learning and reasoning…
