ECO: Entity-level captioning in context

Hyunsouk Cho, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages750-751
Number of pages2
ISBN (Electronic)9781509028467
DOIs
Publication statusPublished - 2016 Nov 21
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: 2016 Aug 182016 Aug 21

Publication series

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

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period16/8/1816/8/21

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

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