ECO: Entity-level captioning in context

Hyunsouk Cho; Seung Won Hwang

doi:10.1109/ASONAM.2016.7752321

ECO: Entity-level captioning in context

Hyunsouk Cho, Seung Won Hwang

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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
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	https://doi.org/10.1109/ASONAM.2016.7752321
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

Other

Other	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
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

Access to Document

10.1109/ASONAM.2016.7752321

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Cho, H., & Hwang, S. W. (2016). ECO: Entity-level captioning in context. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 750-751). [7752321] (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752321

Cho, Hyunsouk ; Hwang, Seung Won. / ECO : Entity-level captioning in context. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 750-751 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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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.",

author = "Hyunsouk Cho and Hwang, {Seung Won}",

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); 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",

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Cho, H & Hwang, SW 2016, ECO: Entity-level captioning in context. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752321, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 750-751, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 16/8/18. https://doi.org/10.1109/ASONAM.2016.7752321

ECO : Entity-level captioning in context. / Cho, Hyunsouk; Hwang, Seung Won.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 750-751 7752321 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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Cho H, Hwang SW. ECO: Entity-level captioning in context. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 750-751. 7752321. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). https://doi.org/10.1109/ASONAM.2016.7752321