The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning to share visual appearance for multiclass object detection
Abstract
We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.
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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
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
Self-citationSeparating Style and Content with Bilinear Models
Tenenbaum, J. B., & Freeman, W. T. (2000) · Neural Computation · doi:10.1162/089976600300015349 · Cited by 840 · cited 1×
“These methods can be applied for object classes with well defined sets of transformations such as letters [20, 28], or faces [28].”
MethodSelf-citation
Cited by
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Self-citationScene Graph Generation by Iterative Message Passing
Xu, D., Zhu, Y., Choy, C. B., & Fei-Fei, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.330 · Cited by 1,271
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., & Yu, Y. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.314 · Cited by 936
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
Chao, W.-L., Changpinyo, S., Gong, B., & Sha, F. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_4 · Cited by 616
Wurm, M., Stark, T., Zhu, X. X., Weigand, M., & Taubenböck, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.006 · Cited by 332
Metadata
- Authors
- Salakhutdinov, R., Torralba, A., & Tenenbaum, J.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1481-1488
- OpenAlex
- W2010132303
- Cited by
- 325
- References
- 4
- FWCI
- 16.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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