Looking behind the Seen in Order to Anticipate
Abstract: Despite significant recent progress in computer vision and machine learning, personalized autonomous agents often still don’t participate robustly and safely across tasks in our environment. We think this is largely because they lack an ability to anticipate, which in turn is due to a missing understanding about what is happening behind the seen, i.e., [...]
Robots that Learn through Language
Abstract: Advances in perception have been integral to transitioning robots from machines restricted to factory automation to autonomous agents that operate robustly in unstructured environments. As our surrogates, robots enable people to explore the deepest depths of the ocean and distant regions of space, making discoveries that would otherwise be impossible. The age of robots [...]
Towards Reconstructing Any Object in 3D
Abstract: The world we live in is incredibly diverse, comprising of over 10k natural and man-made object categories. While the computer vision community has made impressive progress in classifying images from such diverse categories, the state-of-the-art 3D prediction systems are still limited to merely tens of object classes. A key reason for this stark difference [...]
Carnegie Mellon University
Beyond rigid objects: Data-driven Methods for Manipulation of Deformable Objects
Abstract: Manipulation of deformable objects challenges common assumptions made for rigid objects. Deformable objects have high intrinsic state representation and complex dynamics with high degrees of freedom, making it difficult for state estimation and planning. The completed work can be divided into two parts. In the first part, we explore reinforcement learning (RL) as a [...]
Carnegie Mellon University
Simulation, Perception, and Generation of Human Behavior
Abstract: Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects --- simulation, perception, and generation. Throughout this thesis, we show how the three aspects are deeply connected and [...]
The Clinician’s AI Partner: Augmenting Clinician Capabilities Across the Spectrum of Healthcare
Abstract: Clinicians often work under highly demanding conditions to deliver complex care to patients. As our aging population grows and care becomes increasingly complex, physicians and nurses are now also experiencing feelings of burnout at unprecedented levels. In this talk, I will discuss possibilities for computer vision to function as a partner to clinicians, and to augment their capabilities, across [...]
The Unusual Effectiveness of Abstractions for Assistive AI
Abstract: Can we balance efficiency and reliability while designing assistive AI systems? What would such AI systems need to provide? In this talk I will present some of our recent work addressing these questions. In particular, I will show that a few fundamental principles of abstraction are surprisingly effective in designing efficient and reliable AI [...]
Reliable and Accessible Visual Recognition
Abstract: As visual recognition models are developed across diverse applications; we need the ability to reliably deploy our systems in a variety of environments. At the same time, visual models tend to be trained and evaluated on a static set of curated and annotated data which only represents a subset of the world. In this [...]
Fake It Till You Make It: Face analysis in the wild using synthetic data alone
Abstract: In this seminar I will demonstrate how synthetic data alone can be used to perform face-related computer vision in the wild. The community has long enjoyed the benefits of synthesizing training data with graphics, but the domain gap between real and synthetic data has remained a problem, especially for human faces. Researchers have tried [...]
Carnegie Mellon University
Structured Learning for Robust Robot Manipulation
Abstract: Robust and generalizable robots that can autonomously manipulate objects in semi-structured environments can bring material benefits to society. Data-driven learning approaches are crucial for enabling such systems by identifying and exploiting patterns in semi-structured environments, allowing robots to adapt to novel scenarios with minimal human supervision. However, despite significant prior work in learning for [...]