COLLOQUIUM
Zsolt Kira
Abstract
A large number of robot perception tasks have been revolutionized by machine learning and deep neural networks in particular. However, current learning methods are limited in several ways that hinder their large-scale use for critical robotics applications: They are often focused on individual sensor modalities, do not attempt to understand semantic information in a fine-grained temporal manner, and are beholden to strong assumptions about the data (e.g. that the data distribution is the same when deployed in the real world as when trained). In this talk, I will describe work on novel deep learning architectures for moving beyond current methods to develop a richer multi-modal and fine-grained scene understanding from raw sensor data. I will also discuss methods we have developed that can use transfer learning to deal with changes in the environment or the existence of entirely new, unknown categories in the data (e.g. unknown object types). I will focus especially on this latter work, where we use neural networks to learn how to compare objects and transfer such learning to new domains using one of the first deep-learning based clustering algorithms, which we developed. I will show examples of real-world robotic systems using these methods, and conclude by discussing future directions in this area, towards making robots able to continually learn and adapt to new situations as they arise.
Dr. Zsolt Kira received his B.S. in ECE at the University of Miami in 2002 and M.S. and Ph.D. in Computer Science from the Georgia Institute of Technology in 2010. He is currently a Senior Research Scientist and Branch Chief of the Machine Learning and Analytics group at the Georgia Tech Research Institute (GTRI). He is also an Adjunct at the School of Interactive Computing and Associate Director of Georgia Tech’s Machine Learning Center (ML@GT). He conducts research in the areas of machine learning for sensor processing and robot perception, with emphasis on feature learning for multi-modal object detection, video analysis, scene characterization, and transfer learning. He has over 25 publications in these areas, several best paper/student paper and other awards, and has been invited to speak at related workshops in both academia and government venues.
Date: Feb. 28, 2018
Time: 10:15-11:15 am
Place: Innovation Center, Room 2277
Zsolt Kira
Abstract
A large number of robot perception tasks have been revolutionized by machine learning and deep neural networks in particular. However, current learning methods are limited in several ways that hinder their large-scale use for critical robotics applications: They are often focused on individual sensor modalities, do not attempt to understand semantic information in a fine-grained temporal manner, and are beholden to strong assumptions about the data (e.g. that the data distribution is the same when deployed in the real world as when trained). In this talk, I will describe work on novel deep learning architectures for moving beyond current methods to develop a richer multi-modal and fine-grained scene understanding from raw sensor data. I will also discuss methods we have developed that can use transfer learning to deal with changes in the environment or the existence of entirely new, unknown categories in the data (e.g. unknown object types). I will focus especially on this latter work, where we use neural networks to learn how to compare objects and transfer such learning to new domains using one of the first deep-learning based clustering algorithms, which we developed. I will show examples of real-world robotic systems using these methods, and conclude by discussing future directions in this area, towards making robots able to continually learn and adapt to new situations as they arise.
Dr. Zsolt Kira received his B.S. in ECE at the University of Miami in 2002 and M.S. and Ph.D. in Computer Science from the Georgia Institute of Technology in 2010. He is currently a Senior Research Scientist and Branch Chief of the Machine Learning and Analytics group at the Georgia Tech Research Institute (GTRI). He is also an Adjunct at the School of Interactive Computing and Associate Director of Georgia Tech’s Machine Learning Center (ML@GT). He conducts research in the areas of machine learning for sensor processing and robot perception, with emphasis on feature learning for multi-modal object detection, video analysis, scene characterization, and transfer learning. He has over 25 publications in these areas, several best paper/student paper and other awards, and has been invited to speak at related workshops in both academia and government venues.
Date: Feb. 28, 2018
Time: 10:15-11:15 am
Place: Innovation Center, Room 2277