Director of Research (if dissertation) or Advisor (if thesis)
Hoiem, Derek
Doctoral Committee Chair(s)
Hoiem, Derek
Committee Member(s)
Lazebnik, Svetlana
Forsyth, David
Parikh, Devi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer vision
Visual attention
Visual question answering (VQA)
Keypoint localization
Part localization
Image recognition
Fine-grained image recognition
Deep learning
Multi-task learning
Machine learning
Abstract
Knowing where to look in an image can significantly improve performance in computer vision tasks by eliminating irrelevant information from the rest of the input image, and by breaking down complex scenes into simpler and more familiar sub-components. We show that a framework for identifying multiple task-relevant regions can be learned in current state-of-the-art deep network architectures, resulting in significant gains in several visual prediction tasks. We will demonstrate both directly and indirectly supervised models for selecting image regions and show how they can improve performance over baselines by means of focusing on the right areas.
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