Field Robotics Center Seminar
Cedric Scheerlinck
PhD Student
Australian National University

Event Cameras: Image Reconstruction, Convolutions and Color

Newell-Simon Hall 4305

Abstract: Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called ‘events’. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Event cameras do not output conventional image frames, thus, image reconstruction from events enables [...]

Field Robotics Center Seminar
Sierra Young
Assistant Professor
North Carolina State University

From Farm to Takeoff: Ground and Aerial Robots for Biological Systems Analysis

1305 Newell Simon Hall

Abstract: Biological and agricultural environments are dynamic, unstructured, and uncertain, posing challenges for environmental data collection at the necessary spatial and temporal scales to enable meaningful systems analysis. Small unmanned systems, however, can overcome some of these challenges by enabling autonomous or human-assisted image-based and in situ environmental data collection. This talk will present a suite of [...]

VASC Seminar
Aljosa Osep
M.Sc. Computer Science
RWTH Aachen University, Computer Vision Group

Tracking Beyond Detection

GHC 6501

Abstract:  The majority of existing vision-based methods perform multi-object tracking in the image domain. Yet, in mobile robotics and autonomous driving scenarios, pixel-precise object localization and trajectory estimation in 3D space are of fundamental importance. Furthermore, the leading paradigms for vision-based multi-object tracking and trajectory prediction heavily rely on object detectors and effectively limit tracking [...]

VASC Seminar
Yuval Bahat
PhD
Technion - Israel Institute of Technology

Exploiting Deviations from Ideal Visual Recurrence

1305 Newell Simon Hall

Abstract: Visual repetitions are abundant in our surrounding physical world: small image patches tend to reoccur within a natural image, and across different rescaled versions thereof. Similarly, semantic repetitions appear naturally inside an object class within image datasets, as a result of different views and scales of the same object. We studied deviations from these [...]

VASC Seminar
Shu Kong
PhD Candidate
University of California at Irvine

Attending to Pixels, Embedding Pixels, Predicting Pixels

1305 Newell Simon Hall

Abstract: Nowadays splashy applications heavily depend on meticulously annotated datasets, data-driven and learning-based methods, among which pixel labeling plays an important role yet often lacks interpretability. In this talk, I will discuss how we deal with pixels with better interpretability. Firstly, I'll introduce the pixel embedding framework that allows for clustering pixels into discrete groups [...]

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 [...]

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 [...]

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 [...]

RI Seminar
Ross Knepper
Assistant Professor
Department of Computer Science, Cornell University

Formalizing Teamwork in Human-Robot Interaction

Gates Hillman Center 6115

Abstract: Robots out in the world today work for people but not with people. Before robots can work closely with ordinary people as part of a human-robot team in a home or office setting, robots need the ability to acquire a new mix of functional and social skills. Working with people requires a shared understanding [...]

VASC Seminar
Hironobu Fujiyoshi
Professor
Chubu University (Japan)

Knowledge Transfer Graph for Deep Collaborative Learning

3305 Newell-Simon Hall

Abstract:  In this talk I will present our latest research about knowledge transfer graph for Deep Collaborative Learning (DCL), which is a method that incorporates Knowledge Distillation and Deep Mutual Learning. DCL is represented by a directional graph where each model is represented by a node, and the propagation of knowledge from the source node to the [...]