MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Siva Chaitanya Mynepalli

Title: Recognizing Tiny Faces Abstract: Objects are naturally captured over a continuous range of distances, causing dramatic changes in appearance, especially at low resolutions. Recognizing such small objects at range is an open challenge in object recognition. In this paper, we explore solutions to this problem by tackling the fine-grained task of face recognition. State-of-the-art embeddings [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Data Centric Robot Learning

NSH 4305

Abstract: While robotics has made tremendous progress over the last few decades, most success stories are still limited to carefully engineered and precisely modeled environments. Getting these robots to work in the complex and diverse world that we live in has proven to be a difficult challenge. Interestingly, one of the most significant successes in [...]

VASC Seminar
Erik Learned-Miller
Professor
University of Massachusetts, Amherst

Automatically Supervised Learning: Two more steps on a long journey

1305 Newell Simon Hall

Abstract: I will talk about two recent pieces of work that attempt to move towards learning with less reliance on labeled data. In the first, part, I will talk about how the surrogate task of predicting the motion of objects can induce complex representations in neural networks without any labeled data.  In the second part of [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Exploiting Point Motion, Shape Deformation, and Semantic Priors for Dynamic 3D Reconstruction in the Wild

NSH 3002

Abstract: With the advent of affordable and high-quality smartphone cameras, any significant events will be massively captured both actively and passively from multiple perspectives. This opens up exciting opportunities for low-cost high-end VFX effects and large scale media analytics. However, automatically organizing large scale visual data and creating a comprehensive 3D scene model is still [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Learning and Reasoning with Visual Correspondence in Time

NSH 3002

Abstract: There is a famous tale in computer vision: Once, a graduate student asked the famous computer vision scientist Takeo Kanade: "What are the three most important problems in computer vision?" Takeo replied: "Correspondence, correspondence, correspondence!" Indeed, even for the most commonly applied Convolutional Neural Networks (ConvNets), they are internally learning representations that lead to [...]

VASC Seminar
Francesc Moreno Noguer
Associate Researcher
Institut de Robotica i Informatica Industrial (Barcelona, Spain)

Geometric Deep Learning for Perceiving and Modeling Humans

GHC 6501

Abstract: Perceiving and modeling shape and appearance of the human body from single images is a severely under-constrained problem that not only requires large volumes of data, but also prior knowledge.  In this talk I will present recent solutions on how deep learning can leverage on geometric reasoning to address tasks like 3D estimation of [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Forecasting and Controlling Behavior by Learning from Visual Data

NSH 4305

Abstract: Achieving a precise predictive understanding of the future is difficult, yet widely studied in the natural sciences. Significant research activity has been dedicated to building testable models of cause and effect. From a certain view, a perfect predictive model of the universe is the “holy grail”; the ultimate goal of science. If we had [...]

VASC Seminar
Wenshuo Wang
Postdoctoral Research Associate
Safe AI Lab, Carnegie Mellon University

Human-Level Learning of Driving Primitives through Bayesian Nonparametric Statistics

Gates-Hillman Center 8102

Abstract: Understanding and imitating human driver behavior has benefited for autonomous driving in terms of perception, control, and decision-making. However, the complexity of multi-vehicle interaction behavior is far messier than human beings can cope with because of the limited prior knowledge and capability of dealing with high-dimensional and large-scale sequential data. In this talk, I [...]