The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Learning to Localize Objects with Structured Output Regression
Open accessOverview
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
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
A discriminatively trained, multiscale, deformable part model
Felzenszwalb, P. F., McAllester, D., & Ramanan, D. (2008) · 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.2008.4587597 · Cited by 2,972
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079
Support vector machine learning for interdependent and structured output spaces
Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015341 · Cited by 1,493
Representing shape with a spatial pyramid kernel
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423
Sampling Strategies for Bag-of-Features Image Classification
Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927
Combined Object Categorization and Segmentation With An Implicit Shape Model
Leibe, B., Leonardis, A., & Schiele, B. (2004) · Cited by 810
The 2005 PASCAL Visual Object Classes Challenge
Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al. (2006) · Lecture Notes in Computer Science · doi:10.1007/11736790_8 · Cited by 765
Beyond sliding windows: Object localization by efficient subwindow search
Lampert, C. H., Blaschko, M. B., & Hofmann, T. (2008) · 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.2008.4587586 · Cited by 693
Self-citationBakir, G. H., Hofmann, T., Schölkopf, B., Smola, A. J., Taskar, B., & Vishwanathan, S. V. N. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7443.001.0001 · Cited by 579
Groups of Adjacent Contour Segments for Object Detection
Ferrari, V., Fevrier, L., Jurie, F., & Schmid, C. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1144 · Cited by 565
(1995) · Cited by 359
An Exemplar Model for Learning Object Classes
Chum, O., & Zisserman, A. (2007) · 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.2007.383050 · Cited by 256
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
Fergus, R., Perona, P., & Zisserman, A. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-8707-x · Cited by 197
Hierarchical Part-Based Visual Object Categorization
Bouchard, G., & Triggs, B. (2005) · 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.2005.174 · Cited by 131
Integrating representative and discriminant models for object category detection
Fritz, M., Leibe, B., Barbara Caputo, B., & Schiele, B. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.124 · Cited by 124
Predicting Structured Data (Neural Information Processing)
Bakir, G. H., Hofmann, T., Schölkopf, B., Smola, A. J., Taskar, B., & Vishwanathan, S. V. N. (2007) · The MIT Press eBooks · Cited by 93
Detecting lameness using ‘Re-sampling Condensation’ and ‘multi-stream cyclic hidden Markov models’
Magee, D., & Boyle, R. D. (2002) · Image and Vision Computing · doi:10.1016/s0262-8856(02)00047-1 · Cited by 54
Object categorization with SVM: kernels for local features
Eichhorn, J., & Chapelle, O. (2004) · Cited by 51
Techniques for Still Image Scene Classification and Object Detection
Viitaniemi, V., & Laaksonen, J. T. (2006) · Lecture notes in computer science · doi:10.1007/11840930_4 · Cited by 29
Composite Models of Objects and Scenes for Category Recognition
Crandall, D., & Huttenlocher, D. P. (2007) · 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.2007.383155 · Cited by 29
Replay Detection Based on Semi-automatic Logo Template Sequence Extraction in Sports Video
Dang, Z., Du, J., Huang, Q., & Jiang, S. (2007) · International Conference on Image and Graphics · doi:10.1109/icig.2007.73 · Cited by 19
Comparative study of metrics for evaluation of object localisation by bounding boxes.
Hemery, B., Laurent, H., & Rosenberger, C. (2007) · International Conference on Image and Graphics · doi:10.1109/icig.2007.118 · Cited by 14
Research works printed but not linked: 1
- Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Cited by
Sorted by citations
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
“Then, classifiers [35, 21, 13, 10] or localizers [1, 31] are used to identify objects in the feature space.”
MethodStruck: Structured output tracking with kernels
Hare, S., Saffari, A., & Torr, P. H. S. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126251 · Cited by 1,831
Staple: Complementary Learners for Real-Time Tracking
Bertinetto, L., Valmadre, J., Golodetz, S., Mikšík, O., & Torr, P. H. S. (2016) · 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.2016.156 · Cited by 1,825
Berman, M., Triki, A. R., & Blaschko, M. B. (2018) · 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.2018.00464 · Cited by 1,163
Self-citationOptimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation
Rahman, M. A., & Wang, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50835-1_22 · Cited by 1,047
Metadata
- Authors
- Blaschko, M. B., & Lampert, C. H.
- Affiliations
- Max Planck Institute for Biological Cybernetics (DE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Volume
- 5302
- Pages
- 2-15
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1525954826
- Cited by
- 324
- References
- 26
- Citations by year
- 2025: 7 · 2024: 6 · 2023: 8 · 2022: 7 · 2021: 11 · 2020: 5 · 2019: 16 · 2018: 17 · 2017: 22 · 2016: 27 · 2015: 35 · 2014: 31 · 2013: 27 · 2012: 22
- FWCI
- 22.16
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Lecture notes in computer science
- Funders
- Technische Universität Darmstadt
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object localization, bounding box regression, branch and bound, structured training, support vector machine, Pascal VOC
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