The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Utility data annotation with Amazon Mechanical Turk
Abstract
We show how to outsource data annotation to Amazon Mechanical Turk. Doing so has produced annotations in quite large numbers relatively cheaply. The quality is good, and can be checked and controlled. Annotations are produced quickly. We describe results for several different annotation problems. We describe some strategies for determining when the task is well specified and properly priced.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
A Trainable System for Object Detection
Papageorgiou, C., & Poggio, T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008162616689 · Cited by 1,289
Cited by
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845
Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012) · Political Analysis · doi:10.1093/pan/mpr057 · Cited by 4,008
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917
Self-citationCheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613751 · Cited by 1,887
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444
Poselets: Body part detectors trained using 3D human pose annotations
Bourdev, L., & Malik, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459303 · Cited by 961
Who are the crowdworkers?: shifting demographics in mechanical turk
Ross, J., Irani, L., Silberman, M. S., Zaldivar, A., & Tomlinson, B. (2010) · CHI '10 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1753846.1753873 · Cited by 1,004
Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441923 · Cited by 1,034
Soylent: a word processor with a crowd inside
Bernstein, M. S., Little, G., Miller, R. C., Hartmann, B., Ackerman, M. S., Karger, D. R., Crowell, D., & Panovich, K. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866078 · Cited by 686
VizWiz: nearly real-time answers to visual questions
Bigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R. C., Miller, R., Tatarowicz, A., White, B., White, S., et al. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866080 · Cited by 601
Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
Budak, C., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw007 · Cited by 336
Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., Fan, W., & Han, J. (2016) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2897350.2897352 · Cited by 336
Shrestha, Y. R., Krishna, V., & von Krogh, G. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.068 · Cited by 331
Learning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
Mitra, T., Hutto, C. J., & Gilbert, E. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702553 · Cited by 102
Crowds Can Effectively Identify Misinformation at Scale
Martel, C., Allen, J., Pennycook, G., & Rand, D. G. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231190388 · Cited by 84
Veselovsky, V., Ribeiro, M. H., & West, R. (2023) · arXiv · doi:10.48550/arxiv.2306.07899 · Cited by 73
“…MTurk spearheaded a paradigm shift in how machine learning datasets were created (Paullada et al., 2021; Marge et al., 2010) and how user studies (Sorokin and Forsyth, 2008), surveys (Buhrmester et al., 2011), and social science experiments (Pennycook et al., 2021; Salganik, 2019) were conducted.”
Active and semi-supervised learning for object detection with imperfect data
Rhee, P. K., Erdenee, E., Kyun, S. D., Ahmed, M. U., & Jin, S. (2017) · Cognitive Systems Research · doi:10.1016/j.cogsys.2017.05.006 · Cited by 55
Crowdsourcing the Unknown: The Satellite Search for Genghis Khan
Lin, A. Y.-M., Huynh, A., Lanckriet, G., & Barrington, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114046 · Cited by 35
Multiplicity and word sense: evaluating and learning from multiply labeled word sense annotations
Passonneau, R. J., Bhardwaj, V., Salleb-Aouissi, A., & Ide, N. (2012) · Language Resources and Evaluation · doi:10.1007/s10579-012-9188-x · Cited by 34
Barriers to collaborative information seeking in organizations
Karunakaran, A., & Reddy, M. (2012) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.14504901169 · Cited by 8
On the State of Reporting in Crowdsourcing Experiments and a Checklist to Aid Current Practices
Ramírez, J., Sayin, B., Baez, M., Casati, F., Cernuzzi, L., Benatallah, B., & Demartini, G. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479531 · Cited by 4
Metadata
- Authors
- Sorokin, A., & Forsyth, D.
- Venue
- 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2149489787
- Cited by
- 616
- References
- 3
- FWCI
- 48.56
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
