The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Is bottom-up attention useful for object recognition?
Abstract
A key problem in learning multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter which is not associated to the objects. We investigate empirically to what extent pure bottom-up attention can extract useful information about the location, size and shape of objects from images and demonstrate how this information can be utilized to enable unsupervised learning of objects from unlabeled images. Our experiments demonstrate that the proposed approach to using bottom-up attention is indeed useful for a variety of applications.
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Citations
Cites
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
Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Self-citation
Cited by
Harel, J., Koch, C., & Perona, P. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,439
Self-citationSaliency Detection via Graph-Based Manifold Ranking
Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.407 · Cited by 2,554
Visual saliency based on multiscale deep features
Li, G., & Yu, Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299184 · Cited by 1,163
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
Context based object categorization: A critical survey
Galleguillos, C., & Belongie, S. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.02.004 · Cited by 351
Metadata
- Authors
- Rutishauser, U., Walther, D., Koch, C., & Perona, P.
- Venue
- Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
- Publisher
- IEEE
- Published
- 2004-11-13
- Volume
- 2
- Pages
- 37-44
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2117301471
- Cited by
- 592
- References
- 4
- FWCI
- 9.01
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rutishauser, U., Walther, D., Koch, C., & Perona, P. (2004). Is bottom-up attention useful for object recognition? Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. https://doi.org/10.1109/cvpr.2004.1315142
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