Publication
Graphics and multimedia
Microsoft Research Blog
Unlocking new dimensions in image-generation research with Manifold Matching via Metric Learning
Generative image models offer a unique value by creating new images. Such images can be sharp super-resolution versions of existing images or even realistic-looking synthetic photographs. Generative Adversarial Networks (GANs) and their variants have demonstrated…
Video
Fourier Feature Networks and Neural Volume Rendering
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.…
Video
Keynote: ReduNet: Deep (convolutional) networks from the principle of rate reduction
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,…
Video
Closing remarks: Towards Human-Like Visual Learning and Reasoning
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…
Video
Panel: Computer vision in the next decade: Deeper or broader
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…
Video
Opening remarks: Towards Human-Like Visual Learning and Reasoning
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…
Video
Research talks: Generalization and adaptation
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…
