Title: Low-Shot Learning for Object Recognition, Detection, and Segmentation

Amirreza Shaban

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Date: Friday, May 8, 2020

Time: 12:00 pm – 2:00 pm (EST)

Location: https://bluejeans.com/319799468/7613?src=calendarLink

Committee:

Dr. Byron Boots (Advisor,  School of Interactive Computing, Georgia Institute of Technology)

Dr. James Hays (School of Interactive Computing, Georgia Institute of Technology)

Dr. Dhruv Batra (School of Interactive Computing, Georgia Institute of Technology)

Dr. Zsolt Kira (School of Interactive Computing, Georgia Institute of Technology)

Dr. Fuxin Li (School of Electrical Engineering and Computer Science, Oregon State University)

Abstract:

Deep Neural Networks are powerful at solving classification problems in computer vision. However, learning classifiers with these models requires a large amount of labeled training data, and recent approaches have struggled to adapt to new classes in a data-efficient manner. On the other hand, the human brain is capable of utilizing already known knowledge in order to learn new concepts with fewer examples and less supervision. Many few-learning algorithms have been proposed to fill this gap but they come with their practical and theoretical limitations. We review the well-known bi-level optimization as a general framework for few-shot learning and hyperparameter optimization and discuss the practical limitations of computing the full gradient. We provide theoretical guarantees for the convergence of the bi-level optimization using the approximated gradients computed by the truncated back-propagation. In the next step, we propose an empirical method for few-shot semantic segmentation: instead of solving the inner optimization, we propose to directly estimate its result by a general function approximator. We utilize the proposed few-shot semantic segmentation technique to improve the performance of object video segmentation. Finally, we introduce the problem of weakly supervised few-shot object detection and its challenges and propose a few-shot learning framework for this task.