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Cited by 408

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

NEIL: Extracting Visual Knowledge from Web Data

Chen, Xinlei, Shrivastava, Abhinav, Gupta, Abhinav·2013·2013 IEEE International Conference on Computer Vision
DOI 10.1109/iccv.2013.178
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Abstract

We propose NEIL (Never Ending Image Learner), a computer program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from Internet data. NEIL uses a semi-supervised learning algorithm that jointly discovers common sense relationships (e.g., "Corolla is a kind of/looks similar to Car", "Wheel is a part of Car") and labels instances of the given visual categories. It is an attempt to develop the world's largest visual structured knowledge base with minimum human labeling effort. As of 10th October 2013, NEIL has been continuously running for 2.5 months on 200 core cluster (more than 350K CPU hours) and has an ontology of 1152 object categories, 1034 scene categories and 87 attributes. During this period, NEIL has discovered more than 1700 relationships and has labeled more than 400K visual instances.

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Research works printed but not linked: 37

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Cited by

Metadata

Authors
Chen, X., Shrivastava, A., & Gupta, A.
Venue
2013 IEEE International Conference on Computer Vision
Publisher
IEEE
Published
2013-12
Pages
1409-1416
DOI
10.1109/iccv.2013.178
OpenAlex
W1964763677
Cited by
408
References
5
FWCI
34.35
Type
conference paper
Language
en
Open access
closed

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