The DOI matches its Crossref record; 44 of the 50 references it cites were found in a registry or an index.
Distinctive Image Features from Scale-Invariant Keypoints
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In the citing paper
“Moreover, the efficiency of HF-net2 was tested and compared with several wellknown handcrafted algorithms for keypoint detection and description including Harris [4], FAST [6], SIFT [9] and ORB [8].”
From Keypoint Detection and Description through Deep Learning in Unstructured Environments (2023) · cites this paper as [9]
In the citing paper
“By including a background removal step sim-330 ilar to the WGE in [9], which makes use of a Prewitt operator in combination with a gradient filter, the authors could also demonstrate the robustness of their IP with respect to the Earth in the background.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Feature fusion was then used to synthesise the detected points into polylines which resemble parts of the spacecraft body.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Alternatively, Capuano et al. [32] introduced a new IP scheme in which three different parallel processing streams, which use the Shi-Tommasi (ST) corners detector, the HT, and the Line Segment Detector (LSD), are exploited in order to filter three sets of points and improve the robustness of the feature detection.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Furthermore, the scenarios with the Earth horizon were tackled by tuning the threshold of gradient filter to a more selective value.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Robustness proven”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc., its use in image hashing, as proposed in this paper, has proved to be every effective.”
From A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids (2019) · cites this paper as [9]
In the citing paper
“The latter, introduced by Lowe [2], have been shown to outperform the others [7].”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“This can be explained by the fact that they capture a substantial amount of information about the spatial intensity patterns, while at the same time being robust to small deformations or localisation errors.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as Lowe 2004
In the citing paper
“Traditional methods may require complex feature matching algorithms (e.g., SIFT, SURF) or additional classifiers (e.g., SVM), whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, which simplifies the process [27,28].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [27]
Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Self-citationA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Generalizing the Hough transform to detect arbitrary shapes
Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,434
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,764
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Witkin, A. (1987) · Elsevier eBooks · doi:10.1016/b978-0-08-051581-6.50036-2 · Cited by 2,375
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,046
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
Cited by
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Sarin, S., Fahrmair, M., Wagner, M. W., et al. (2012) · Journal of Information Processing · doi:10.2197/ipsjjip.20.250 · Cited by 1
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Halawani, A., Tamimi, H., Burkhardt, H., et al. (2006) · Lecture notes in computer science · doi:10.1007/11867661_1 · Cited by 1
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Che, S., Chen, D., & Wang, X. (2016) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2016.7852741 · Cited by 1
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de Villiers, J. (2015) · Open University of Cape Town (University of Cape Town) · Cited by 1
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Meng, H., Lu, H., Ding, B., & Wang, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129567 · Cited by 1
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Agarwal, M., Vatsa, M., & Singh, R. (2017) · Cited by 1
Glocal Retriever: Glocal Image Retrieval Using the Combination of Global and Local Descriptors
Kim, Z., Kim, Y., & Suh, Y.-J. (2023) · Electronics · doi:10.3390/electronics12020442 · Cited by 1
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Araújo, A. D. M., Silveira, F., Lakshman, H., et al. (2012) · Cited by 1
Structure context of local features in realistic human action recognition
Wu, Q., Lu, S., Wang, Z., et al. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130427 · Cited by 1
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De, J. C. M. R. D. (2019) · doi:10.70675/8fba07e1z9be1z48fcz8adbzac1c5033283a · Cited by 1
Robust object detection scheme using feature selection
Pan, H., Xia, L., & Nguyen, T. Q. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5653519 · Cited by 1
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Ang, L.-M., Seng, K. P., Chew, L. W., et al. (2013) · Springer eBooks · doi:10.1007/978-3-642-38203-1_7 · Cited by 1
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Chacon, G., de Albuquerque, M. P., Gastardelli, E., et al. (2011) · Notas Técnicas · doi:10.7437/nt2236-7640.2011.02.002 · Cited by 1
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Ge, J., Zhang, Y., Li, X., et al. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-3-w2-2022-17-2022 · Cited by 1
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Liu, S., Kong, L., Fang, K., Wang, J., & Xu, C. (2014) · doi:10.1109/icosp.2014.7015119 · Cited by 2
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Chen, G. (2022) · Mobile Information Systems · doi:10.1155/2022/6858822 · Cited by 1
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Zhang, P., & Liu, W. (2023) · Applied Sciences · doi:10.3390/app13021076 · Cited by 1
Wantuch, A. C., Vita, J. A., Jimenez, E. S., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2237211 · Cited by 1
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Luo, J., Orabona, F., Caputo, B., et al. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1
Díaz, A. A. O., Oliveira, D. N. D., & Tamayo, S. C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35989-7_38 · Cited by 1
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Lai, T., Wang, D., Xiao, G., et al. (2014) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2014.7064452 · Cited by 1
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Zhang, Y., Jiang, S. L., & Tang, X. (2023) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00065 · Cited by 1
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Wang, X., & Li-ping, M. (2011) · International Conference on Electronics, Communications and Control · doi:10.1109/icecc.2011.6066606 · Cited by 1
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Shirude, R., Khurana, S., Sreema, R., et al. (2026) · Automatic Control and Computer Sciences · doi:10.3103/s0146411626700045 · Cited by 1
“According to the research, an approach to using these features for object recognition uses a fast nearest-neighbor algorithm, the Hough transform, and a least squares solution [7].”
Decision fusion of global and local image features for Markov localization
Zhao, Z. (2009) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2009.5207436 · Cited by 1
Image based region recognition in gastrointestinal endoscopy
Frączek, R., & Duplaga, M. (2010) · Bio-Algorithms and Med-Systems · Cited by 1
Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition
Lee, J., Min, H., Seo, J.-J., et al. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890317 · Cited by 1
RandommaskFormer: Light Weight Remote Sensing Scene Classification with Masked Transformer
Hu, X., Wu, W., & Zhu Li, B. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700166 · Cited by 1
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Fezza, S. A., Larabi, M., & Faraoun, K. M. (2013) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1
Bi, Y., Xue, B., & Zhang, M. (2020) · doi:10.26686/wgtn.13158329.v1 · Cited by 1
Vision-based guidance of a robotic arm for object handling operations — The white'R vision framework
Theoharatos, C., Kastaniotis, D., Besiris, D., et al. (2016) · IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow · doi:10.1109/rtsi.2016.7740608 · Cited by 1
Self-trainable and adaptive sensor intelligence for selective data generation
Rezvani, A., Huang, W., Chen, H., Ni, Y., & Imani, M. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1403187 · Cited by 2
“Another approach involves transmitting extracted features from the raw data (Redondi et al., 2013), utilizing feature extraction techniques such as SIFT (Lowe, 2004), SURF (Bay et al., 2006), or BRISK (Leutenegger et al., 2011).”
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Ventura, C., Giró-i-Nieto, X., Vilaplana, V., et al. (2015) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 1
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Dynamic model behavior analysis of small groups based on particle video
Zhang, D., Xu, J., Lü, Y., et al. (2013) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2013.6677081 · Cited by 1
Scale-aware co-visible region detection for image matching
Pan, X. H., Xia, Z., & Zheng, X. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.08.015 · Cited by 1
Auxiliary Attribute Aided Few-shot Representation Learning for Gun Image Retrieval
Zhou, Z., Zhang, S., Wu, J., Li, Y., Wang, X., & Peng, S. (2020) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei51763.2020.9263507 · Cited by 1
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Can, G., Odobez, J., Gayol, C. P., et al. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1
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Zhong, J., Leí, T., Yao, G., et al. (2017) · Optical Review · doi:10.1007/s10043-017-0334-y · Cited by 1
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Boult, T. E., & Eckmann, M. (2007) · Cited by 1
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Sundarambal, B., Subramanian, S., & Muthukumar, B. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02129-1 · Cited by 1
RetractedOnline topological segmentation of visual sequences using the algebraic connectivity of graphs
Boal, J., & Miralles, Á. S. (2015) · Robotica · doi:10.1017/s0263574715000053 · Cited by 1
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Pedersen, L., Smith, D. E., Deans, M. C., et al. (2005) · Cited by 1
Query expansion for VHR image detection
Zheng, H., Zhang, H., Bai, X. P., et al. (2011) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2011.6048928 · Cited by 1
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Sardana, H. K., Meenu, N. A., & Jain, T. (2018) · International Journal of Intelligent Machines and Robotics · doi:10.1504/ijimr.2018.10011942 · Cited by 1
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Zhai, A., Clements, M., & Zakhor, A. (2014) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2014.79 · Cited by 1
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Pambudi, E. A., Fauzan, A., Abid Yanuar, B., et al. (2022) · AIP conference proceedings · doi:10.1063/5.0106221 · Cited by 1
Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets
Mensink, T., Verbeek, J. J., Perronnin, F., & Csurka, G. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-5520-1_9 · Cited by 2
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Rees, G., Tutubalina, O., Bjørndahl, M., et al. (2025) · Remote Sensing · doi:10.3390/rs18010045 · Cited by 1
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Wang, X., Zhang, H., & Peng, G. (2022) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-022-01575-7 · Cited by 1
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Scherer, R. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-12195-2_2 · Cited by 1
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Duarte, D. A. V. A., & Gonçalves, G. R. (2015) · Imprensa da Universidade de Coimbra eBooks · doi:10.14195/978-989-26-0983-6_42 · Cited by 1
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Wei, L. (2015) · Cited by 1
Wang, Y., Chao, H., Zhang, Y., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328052 · Cited by 1
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Gao, C., Gao, Y., Weng, D., et al. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3683096 · Cited by 1
Mennillo, L., Royer, E., Mondot, F., et al. (2017) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1
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“Very early, methods [14], [15], [16] with handcrafted features prove ine ff ective due to the complex marine scenes.”
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Taha, M. M. A. (2000) · doi:10.15760/etd.7391 · Cited by 1
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Yang, D., Wang, H., Zhang, X., et al. (2010) · Acta Automatica Sinica · doi:10.3724/sp.j.1004.2010.00817 · Cited by 1
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SciceVPR: Stable cross-image correlation enhanced model for visual place recognition
Wan, S., Wei, Y., Kang, L., et al. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132539 · Cited by 1
“Traditional VPR methods transform query and database images into global features using aggregation algorithms like VLAD [30] and Bag-of-Words [31], which aggregate hand-crafted local descriptors [32], [33].”
Object Localization using Bearing Only Visual Detection
Kristoffer, S., Paul, C., & Jensfelt, P. (2008) · IOS Press eBooks · doi:10.3233/978-1-58603-887-8-254 · Cited by 1
A coarse-to-precise matching strategy in navigation
Liu, H., Geng, B., Wang, Z., et al. (2016) · doi:10.1109/cgncc.2016.7829128 · Cited by 1
Video Stabilization Using SIFT Features, Fuzzy Clustering, and Kalman Filtering
Veon, K. L. (2011) · Digital Commons - DU (University of Denver) · Cited by 1
Scale-rotation invariant features from Non-Subsampled Contourlets
Majumdar, A. (2015) · International Conference on Advances in Pattern Recognition · doi:10.1109/icapr.2015.7050648 · Cited by 1
Jovhari, N., Shah–Hosseini, R., Hasanlou, M., et al. (2024) · Journal of Geospatial Information Technology · doi:10.61186/jgit.12.2.59 · Cited by 1
Quantitative Analysis of Object Detectors for Autonomous Driving and Autonomous Parking
Gohil, P., Plöger, P. G., & Hinkenjann, A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956080 · Cited by 1
Learning to Represent Handwritten Shapes and Words for Matching and Recognition
Almazán, J. (2015) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.728 · Cited by 1
Mathuria, A., & Hammerstrom, D. W. (2019) · International Conference on Neuromorphic Systems (ICONS) · doi:10.1145/3354265.3354286 · Cited by 1
Learning rotation-invariant binary codes for efficient object detection from remote sensing images
Liu, Y., Xu, H., & Sun, Q. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.036504 · Cited by 1
Metadata
- Authors
- Lowe, D. G.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Lowe, David G.
- ORCID
- Lowe, David G.
- Venue
- International Journal of Computer Vision
- Published
- 2004-11
- Volume
- 60
- Issue
- 2
- Pages
- 91-110
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2151103935
- Semantic Scholar
- 8c04f169203f9e55056a6f7f956695babe622a38
- Cited by
- 55,589
- References
- 50
- Citations by year
- 2026: 1480 · 2025: 2400 · 2024: 2519 · 2023: 2604 · 2022: 2705 · 2021: 3066 · 2020: 2933 · 2019: 3240 · 2018: 3232 · 2017: 3363 · 2016: 3431 · 2015: 3747 · 2014: 3569 · 2013: 3333 · 2012: 3003
- FWCI
- 699.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- feature matching, affine invariance, rotation invariance, object recognition, nearest neighbor matching, Hough transform, pose estimation, occlusion
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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