Abstract
Visual scene understanding has been one of the major goals of computer vision. However, existing work has focused on the object-level understanding, which limits the visual questions that can be answered. The goal of this paper is to invite collective efforts for entity-level understanding of images, by releasing ECO datasets and baselines for this task.
Original language | English |
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Title of host publication | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
Editors | Ravi Kumar, James Caverlee, Hanghang Tong |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 750-751 |
Number of pages | 2 |
ISBN (Electronic) | 9781509028467 |
DOIs | |
Publication status | Published - 2016 Nov 21 |
Event | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: 2016 Aug 18 → 2016 Aug 21 |
Publication series
Name | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
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Other
Other | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
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Country | United States |
City | San Francisco |
Period | 16/8/18 → 16/8/21 |
Bibliographical note
Funding Information:This work was partly supported by Institute for Information and communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and the Yonsei University Futureleading Research Initiative of 2015 (RMS2 2015-11-0068)
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Sociology and Political Science
- Communication