Keynote Speakers

MIPR 2024 Keynote Speakers

Keynote Speak: Peyman Milanfar (Google)

Title: The Surprising Power of Denoising link

Abstract: Denoising is one of the oldest problems in imaging. But besides removing noise, we've found completely unexpected, brand new uses for denoising. I will give a whirlwind tour of the structure of denoisers and their properties, and describe why denoising is more important than ever; especially now as a core engine and building block for much more complex tasks in imaging, inverse problems, and machine learning.

Biography: Peyman is a Distinguished Scientist at Google Research, where he leads the Computational Imaging team. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz for 15 years, two of those as Associate Dean for Research. From 2012-2014 he was on leave at Google-x, where he helped develop the imaging pipeline for Google Glass.  Over the last decade, Peyman's team at Google has developed several core imaging technologies at Google that are used in many products. Among these are the zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution (Super Res Zoom) pipeline, and several generations of state of the art digital upscaling algorithms.  Most recently, his team led the development of the Unblur feature launched in Google Photos for Pixel devices.

Peyman received his undergraduate education in electrical engineering and mathematics from the UC Berkeley, and the MS and PhD degrees in electrical engineering from the MIT. He holds more than two dozen patents. He founded MotionDSP, which was acquired by Cubic Inc. 

Along with his students and colleagues, he has won multiple best paper awards for introducing kernel regression in imaging; the RAISR upscaling algorithm; NIMA: neural image quality assessment, and Regularization by Denoising (RED). He's been a Distinguished Lecturer of the IEEE Signal Processing Society, and is a Fellow of the IEEE "for contributions to inverse problems and super-resolution in imaging" 

Keynote Speaker: Ming-Hsuan Yang  (University of California, Merced)

Title: Recent Results on Video Understanding and Generation via Multimodal Foundation Models link 

Abstract: Recent years have witnessed significant advances in vision and language models for various visual tasks, including understanding and generation. In this talk, I will present our recent results on exploiting large vision and language models for video understanding and generation. I will describe our recent work on foundation models for visual classification, video-text retrieval, visual caption, visual query answering, visual grounding, video generation, stylization, outpainting, and video-to-audio tasks.

Biography: Ming-Hsuan Yang is a Professor at UC Merced and a Research Scientist with Google. He received the Google Faculty Award in 2009 and CAREER Award from the National Science Foundation in 2012. Yang received paper awards at UIST 2017, CVPR 2018, ACCV 2018, and Longuet-Higgins Prize in CVPR 2023. He is an Associate Editor-in-Chief of PAMI and Associate Editor of IJCV. He was the Editor-in-Chief of CVIU. Yang served as the Program Chair for ACCV 2014 and ICCV 2019 and Senior Area Chair/Area Chair for CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, IJCAI, and AAAI. Yang is a Fellow of the IEEE and ACM.

Keynote Speaker: Ravi Ramamoorthi, Ronald L. Graham Professor of Computer Science, University of California San Diego; Distinguished Research Scientist, NVIDIA

Title: Neural Radiance Fields for View Synthesis and Beyond link 

Abstract: Applications in augmented reality, 3D photography, immersive experiences and appearance acquisition require solving the view synthesis problem - given a few images of an object or scene of interest, how can we synthesize images from new viewpoints.  This is a fundamental problem in computer vision and graphics, and can be encapsulated as reconstructing the light field of all light passing through a scene from a set of observations.  In this talk, I will briefly describe the early history of the problem, and a series of efforts my group has made in light field synthesis from sparse images, ultimately leading to the now widely used neural radiance field representation.  I discuss the impact of this work and follow-ups, leading to newer work from my group on personalized avatars, enabling real-time radiance fields or live 3D portraits from a single image.

Biography: Ravi Ramamoorthi is the Ronald L. Graham Professor of Computer Science at UCSD and founding director of the UC San Diego Center for Visual Computing.  He earlier held tenured faculty positions at UC Berkeley and Columbia University, in all of which he played a key leadership role in building multi-faculty researcher groups recognized as leaders in computer vision and graphics.  He has authored more than 200 refereed publications, and won 20 major awards, including the ACM SIGGRAPH Significant New Researcher Award, the Presidential Early Career Award for Scientists and Engineers, being named a fellow of IEEE, ACM and the SIGGRAPH Academy, and an inaugural Frontiers of Science Award.  His educational efforts were twice honored with the edX Prize Certificate for exceptional contributions in online teaching and learning, and ten of his students and collaborators have won major dissertation awards including the ACM Dissertation Award Honorable Mention, the ACM SIGGRAPH Outstanding Dissertation Award and the UCSD Chancellor's Dissertation Medal.  In industry, Prof. Ramamoorthi has consulted with Pixar and startups in computational imaging, and currently holds a part-time appointment as a Distinguished Research Scientist at NVIDIA.