Detecting incidents, accelerating dataset annotation, and estimating depth with multi-view invariants
Name
Weber-ejweber-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
Size
71.58 MB
Format
Adobe PDF
Checksum (MD5)
09d948cc15c4a56c7dd36a1ddd6d1f7e
Author(s)
Weber, Ethan
Advisor(s)
Torralba, Antonio
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Computer vision has seen incredible growth since the introduction of large datasets, deep neural networks, and modern computing resources. Current algorithms can perform scene understanding, or the ability to understand and interpret the world through visual perception (e.g., images or videos). In this thesis, we push the boundaries of current scene understanding algorithms with three distinct projects. (1) In the first project, we address limitations of current algorithms to understand natural disasters, damage, and incidents through images. To do this, we create the Incidents Dataset, train a detection model, and present applications to identify incidents in social media streams to inform emergency responders during disaster relief situations. (2) In the second project, we address the issue of costly dataset construction and present a novel framework that reduces the cost of creating large-scale instance annotation datasets. (3) In the third and final project, we move to 3D scene understanding and present an intuitive technique to train monocular depth estimation networks by enforcing consistency of multi-view geometric invariants between image pairs observing the same scene or objects from the same category.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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