Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 592

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

Van Horn, Grant, Branson, Steve, Farrell, Ryan, Haber, Scott, Barry, Jessie, Ipeirotis, Panos, Perona, Pietro, Belongie, Serge·2015·2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2015.7298658
Open this paper in CoChat — free account

Abstract

We introduce tools and methodologies to collect high quality, large scale fine-grained computer vision datasets using citizen scientists - crowd annotators who are passionate and knowledgeable about specific domains such as birds or airplanes. We worked with citizen scientists and domain experts to collect NABirds, a new high quality dataset containing 48,562 images of North American birds with 555 categories, part annotations and bounding boxes. We find that citizen scientists are significantly more accurate than Mechanical Turkers at zero cost. We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%. Nevertheless, we found that learning algorithms are surprisingly robust to annotation errors and this level of training data corruption can lead to an acceptably small increase in test error if the training set has sufficient size. At the same time, we found that an expert-curated high quality test set like NABirds is necessary to accurately measure the performance of fine-grained computer vision systems. We used NABirds to train a publicly available bird recognition service deployed on the web site of the Cornell Lab of Ornithology.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr.2015.7298658

Cites

6 of 6 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 6 references

Research works printed but not linked: 33

  1. 10.1016/j.chb.2010.04.011 · DOI · Paper page
  2. whitehill, 2009, Whose vote should count more: Optimal integration of labels from labelers of unknown expertise, Advances in neural information processing systems, 2035
  3. 10.1109/MC.2006.196 · DOI · Paper page
  4. 10.1109/CVPR.2014.220 · DOI · Paper page
  5. 10.1109/CVPRW.2008.4562953 · DOI · Paper page
  6. 10.1145/1401890.1401965 · DOI · Paper page
  7. 10.1109/CVPRW.2010.5543189 · DOI · Paper page
  8. welinder, 2010, The Multidimensional Wisdom of Crowds, Advances in Neural Information Processing Systems 23, 2424
  9. wah, 2011, The Caltech-Ucsd Birds-200–2011 Dataset, Technical Report CNS-TR-2011-001 California Institute of Technology
  10. 10.1126/science.1160379 · DOI · Paper page
  11. girshick, 2013, Rich feature hierarchies for accurate object detection and semantic segmentation, arXiv preprint arXiv 1311 2524
  12. griffin, 2007, Caltech-256 Object Category Dataset, Tech Rep CNS-TR-2007–001
  13. ipeirotis, 2014, Quizz targeted crowd-sourcing with a billion (potential) users, 143
  14. 10.1007/s10618-013-0306-1 · DOI
  15. khosla, 2011, Novel dataset for fine-grained image categorization, First Workshop on Fine-Grained Visual Categorization IEEE Conference on Computer Vision and Pattern Recognition
  16. krause, 2013, Collecting a Large-Scale Dataset of Fine-Grained Cars, Second Workshop on Fine-Grained Visual Categorization (FGVC2)
  17. 10.1145/1215942.1215943 · DOI
  18. 10.5244/C.18.98 · DOI · Paper page
  19. 10.1145/1553374.1553488 · DOI · Paper page
  20. 10.1016/j.ecoinf.2013.10.006 · DOI
  21. 10.1109/ICCV.2011.6126398 · DOI
  22. berg, 2010, Automatic attribute discovery and characterization from noisy web data, Proceedings of the 11th European Conference on Computer Vision Part V ECCV'10, 663
  23. branson, 2014, Bird species categorization using pose normalized deep convolutional nets, arXiv preprint arXiv 1406 2952
  24. 10.1109/CVPR.2013.81 · DOI
  25. 10.1109/CVPR.2014.259 · DOI · Paper page
  26. 10.1109/TPAMI.2006.79 · DOI · Paper page
  27. 10.1109/CVPR.2013.110 · DOI
  28. lin, 2014, Microsoft coco: Common objects in context, arXiv preprint arXiv 1405 0312
  29. 10.1109/ICCV.2013.373 · DOI
  30. liu, 2012, Dog Breed Classification Using Part Localization, ECCV
  31. 10.1109/CVPR.2009.5206574 · DOI
  32. maji, 2013, Fine-grained visual classification of aircraft, Technical Report
  33. 10.1145/1297797.1297798 · DOI · Paper page

Cited by

See all 592

Metadata

Authors
Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., & Belongie, S.
Venue
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2015-06
Pages
595-604
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2015.7298658
OpenAlex
W1954152232
Cited by
592
References
6
FWCI
6.51
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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. https://app.cochat.ai/papers/10.1109/cvpr.2015.7298658
The paper
Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., & Belongie, S. (2015). Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298658

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.