The DOI matches its Crossref record.
The segmented and annotated IAPR TC-12 benchmark
Overview
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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Self-check
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Citations
Cited by
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582
ReferItGame: Referring to Objects in Photographs of Natural Scenes
Kazemzadeh, S., Ordonez, V., Matten, M., & Berg, T. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1086 · Cited by 1,048
Natural Language Object Retrieval
Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., & Darrell, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.493 · Cited by 532
Cross-Modal Self-Attention Network for Referring Image Segmentation
Ye, L., Rochan, M., Liu, Z., & Wang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01075 · Cited by 532
Segmentation from Natural Language Expressions
Hu, R., Rohrbach, M., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_7 · Cited by 438
Metadata
- Authors
- Escalante, H. J., Hernández, C. A., Gonzalez, J. A., López-López, A., Montes, M., Morales, E. F., Enrique Sucar, L., Villaseñor, L., & Grubinger, M.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 114
- Issue
- 4
- Pages
- 419-428
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2006147162
- Cited by
- 399
- References
- 0
- FWCI
- 12.83
- Type
- journal article
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The segmented and annotated IAPR TC-12 benchmark. https://app.cochat.ai/papers/10.1016/j.cviu.2009.03.008
- The paper
- Escalante, H. J., Hernández, C. A., Gonzalez, J. A., López-López, A., Montes, M., Morales, E. F., Enrique Sucar, L., Villaseñor, L., & Grubinger, M. (2010). The segmented and annotated IAPR TC-12 benchmark. Computer Vision and Image Understanding. https://doi.org/10.1016/j.cviu.2009.03.008
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.
