Understanding language through visual imagination
Name
1145123680-MIT.pdf
Size
3.94 MB
Format
Adobe PDF
Checksum (MD5)
a75f90674640c01345c7048f35699afa
Author(s)
Mao, Cheahuychou.
Advisor(s)
Boris Katz.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
This thesis introduces a multimodal approach to natural language understanding by presenting a generative language-vision model that can generate videos for sentences and a comprehensive approach for using this capability to solve natural language inference, video captioning and video completion without task-specific training. The only training required is for acquiring a lexicon from captioned videos similar to the way children learn language through exposure to perceptual cues. The model generates videos by sampling the visual features of objects described in the target sentences over time. The evaluation results show that the model can reliably generate videos for sentences describing multiple concurrent and sequential actions, and that the ability to reason about language using visual scenes enables language tasks to be reduced to vision tasks and be solved more robustly using information obtained via vision.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 57-60).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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