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
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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Cited by
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
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VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988
Deep learning for visual understanding: A review
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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
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Self-citation
Metadata
- Authors
- Chen, X., Shrivastava, A., & Gupta, A.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 1409-1416
- OpenAlex
- W1964763677
- Cited by
- 408
- References
- 5
- FWCI
- 34.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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