VASC Seminar
Shengjie Zhu
Ph.D. Student
Michigan State University

Structure-from-Motion Meets Self-supervised Learning

Newell-Simon Hall 3305

Abstract: How to teach machine to perceive 3D world from unlabeled videos? We will present new solution via incorporating Structure-from-Motion (SfM) into self-supervised model learning. Given RGB inputs, deep models learn to regress depth and correspondence. With the two inputs, we introduce a camera localization algorithm that searches for certified global optimal poses. However, the [...]

VASC Seminar
Qi Sun
Assistant Professor
New York University

Toward Human-Centered XR: Bridging Cognition and Computation

Newell-Simon Hall 3305

Abstract:   Virtual and Augmented Reality enables unprecedented possibilities for displaying virtual content, sensing physical surroundings, and tracking human behaviors with high fidelity. However, we still haven't created "superhumans" who can outperform what we are in physical reality, nor a "perfect" XR system that delivers infinite battery life or realistic sensation. In this talk, I will discuss some of our [...]

Seminar
C. Karen Liu
Professor
Computer Science Department, Stanford University

Carnegie Mellon Graphics Colloquium: C. Karen Liu : Building Large Models for Human Motion

Rashid Auditorium - 4401 Gates and Hillman Centers

Building Large Models for Human Motion Large generative models for human motion, analogous to ChatGPT for text, will enable human motion synthesis and prediction for a wide range of applications such as character animation, humanoid robots, AR/VR motion tracking, and healthcare. This model would generate diverse, realistic human motions and behaviors, including kinematics and dynamics, [...]

RI Seminar
Dr. Michael Yip
Associate Professor
Dept. of Electrical and Computer Engineering, The University of California San Diego

Teaching a Robot to Perform Surgery: From 3D Image Understanding to Deformable Manipulation

1305 Newell Simon Hall

Abstract: Robot manipulation of rigid household objects and environments has made massive strides in the past few years due to the achievements in computer vision and reinforcement learning communities. One area that has taken off at a slower pace is in manipulating deformable objects. For example, surgical robotics are used today via teleoperation from a [...]

VASC Seminar
Yanxi Liu
Professor
Penn State University

Zeros for Data Science

Newell-Simon Hall 3305

Abstract: The world around us is neither totally regular nor completely random. Our and robots’ reliance on spatiotemporal patterns in daily life cannot be over-stressed, given the fact that most of us can function (perceive, recognize, navigate) effectively in chaotic and previously unseen physical, social and digital worlds. Data science has been promoted and practiced [...]

VASC Seminar
Agata Lapedriza
Principal Research Scientist/Professor
Northeastern University

Emotion perception: progress, challenges, and use cases

Newell-Simon Hall 3305

Abstract: One of the challenges Human-Centric AI systems face is understanding human behavior and emotions considering the context in which they take place. For example, current computer vision approaches for recognizing human emotions usually focus on facial movements and often ignore the context in which the facial movements take place. In this presentation, I will [...]

VASC Seminar
Yunzhu Li
Assistant Professor
University of Illinois Urbana-Champaign

Foundation Models for Robotic Manipulation: Opportunities and Challenges

Newell-Simon Hall 3305

Abstract: Foundation models, such as GPT-4 Vision, have marked significant achievements in the fields of natural language and vision, demonstrating exceptional abilities to adapt to new tasks and scenarios. However, physical interaction—such as cooking, cleaning, or caregiving—remains a frontier where foundation models and robotic systems have yet to achieve the desired level of adaptability and [...]

RI Seminar
Simon Lucey
Director, Australian Institute for Machine Learning (AIML)
Professor, University of Adelaide

Learning with Less

3305 Newell-Simon Hall

Abstract: The performance of an AI is nearly always associated with the amount of data you have at your disposal. Self-supervised machine learning can help – mitigating tedious human supervision – but the need for massive training datasets in modern AI seems unquenchable. Sometimes it is not the amount of data, but the mismatch of [...]

RI Seminar
Kim Baraka
Assistant Professor
Department of Computer Science, Vrije Universiteit Amsterdam

Why We Should Build Robot Apprentices And Why We Shouldn’t Do It Alone

1305 Newell Simon Hall

Abstract: For robots to be able to truly integrate human-populated, dynamic, and unpredictable environments, they will have to have strong adaptive capabilities. In this talk, I argue that these adaptive capabilities should leverage interaction with end users, who know how (they want) a robot to act in that environment. I will present an overview of [...]

RI Seminar
Jia Deng
Associate Professor
Department of Computer Science, Princeton University

Toward an ImageNet Moment for Synthetic Data

1305 Newell Simon Hall

Abstract:  Data, especially large-scale labeled data, has been a critical driver of progress in computer vision. However, many important tasks remain starved of high-quality data. Synthetic data from computer graphics is a promising solution to this challenge, but still remains in limited use. This talk will present our work on Infinigen, a procedural synthetic data [...]