Predictive vision
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/112001 |
| _version_ | 1826192849536811008 |
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| author | Vondrick, Carl (Carl Martin) |
| author2 | Antonio Torralba. |
| author_facet | Antonio Torralba. Vondrick, Carl (Carl Martin) |
| author_sort | Vondrick, Carl (Carl Martin) |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T09:29:49Z |
| format | Thesis |
| id | mit-1721.1/112001 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:29:49Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1120012019-04-12T22:43:14Z Predictive vision Vondrick, Carl (Carl Martin) Antonio Torralba. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 95-106). Anticipating outcomes is the root of intelligence. This thesis investigates Predictive Vision with the goal to develop robust methods that anticipate the next events that may happen in images or videos. Importantly, we develop methods for eciently scaling learning algorithms to learn an extensive set of rules that enable richer visual understanding. While large annotated datasets fuel progress in object recognition, the knowledge required for event understanding is vast and potentially ambiguous. To tackle this challenge, we develop predictive vision algorithms that instead learn these rules directly from large amounts of raw, unlabeled data. Capitalizing on millions of natural videos, this work develops algorithms that learn to anticipate the visual future, forecast human actions, and recognize ambient sounds. by Carl Vondrick. Ph. D. 2017-10-30T15:03:50Z 2017-10-30T15:03:50Z 2017 2017 Thesis http://hdl.handle.net/1721.1/112001 1006381602 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 106 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Vondrick, Carl (Carl Martin) Predictive vision |
| title | Predictive vision |
| title_full | Predictive vision |
| title_fullStr | Predictive vision |
| title_full_unstemmed | Predictive vision |
| title_short | Predictive vision |
| title_sort | predictive vision |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/112001 |
| work_keys_str_mv | AT vondrickcarlcarlmartin predictivevision |