Natural language image description: data, models, and evaluation
Hodosh, Micah A
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https://hdl.handle.net/2142/89008
Description
Title
Natural language image description: data, models, and evaluation
Author(s)
Hodosh, Micah A
Issue Date
2015-11-25
Director of Research (if dissertation) or Advisor (if thesis)
Hockenmaier, Julia
Doctoral Committee Chair(s)
Hockenmaier, Julia
Committee Member(s)
Dolan, Bill
Forsyth, David
Roth, Dan
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
Natural Language Processing
Image Description
Neural Networks
computer vision (CV)
natural language processing (NLP)
Machine Learning
Machine Learning Applications
Image Captioning
Abstract
Automatically describing an image with a concise natural language description
is an ambitious and emerging task bringing together the Natural Language
and Computer Vision communities. With any emerging task, the
necessary groundwork developing appropriate datasets, strong baseline models,
and evaluation frameworks is key. In this thesis, we introduce the rst
large datasets speci cally designed with image description in mind, focusing
on concrete descriptions that can be gleaned from the image alone. Furthermore,
we develop strong baseline models that show the need to model
language beyond a simple bag-of-words approach to increase performance.
Most importantly, we introduce a ranking based framework for comparing
image description models. We show that this framework is more reliable and
accurate than the conventional wisdom of evaluating on novel model generated
text. As this task has gained popularity recently, we further analyze
the drawbacks of current evaluation methods, and put forth concrete extensions
to our ranking framework that will guide progress towards modeling
the association of natural language and the images the language describes.
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