The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Exploiting hierarchical context on a large database of object categories
Open accessAbstract
There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. Context models can efficiently rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit from using context models has been limited because most of these methods were tested on datasets with only a few object categories, in which most images contain only one or two object categories. In this paper, we introduce a new dataset with images that contain many instances of different object categories and propose an efficient model that captures the contextual information among more than a hundred of object categories. We show that our context model can be applied to scene understanding tasks that local detectors alone cannot solve.
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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
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073232 · Cited by 616
Cited by
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Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.119 · Cited by 1,438
Image retrieval using scene graphs
Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298990 · Cited by 1,101
Unified Deep Supervised Domain Adaptation and Generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., & Doretto, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.609 · Cited by 803
Domain Generalization for Object Recognition with Multi-task Autoencoders
Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.293 · Cited by 664
Episodic Training for Domain Generalization
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z., & Hospedales, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00153 · Cited by 435
Metadata
- Authors
- Choi, M. J., Lim, J. J., Torralba, A., & Willsky, A. S.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010
- Pages
- 129-136
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1982522767
- Cited by
- 304
- References
- 2
- FWCI
- 21.72
- Type
- conference paper
- Language
- en
- Open access
- green
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- Choi, M. J., Lim, J. J., Torralba, A., & Willsky, A. S. (2010). Exploiting hierarchical context on a large database of object categories. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2010.5540221
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