The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Cross-domain video concept detection using adaptive svms
Abstract
Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.
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Cites
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Cited by
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
Transfer Learning for Visual Categorization: A Survey
Shao, L., Zhu, F., & Li, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2330900 · Cited by 895
Metadata
- Authors
- Yang, J., Yan, R., & Hauptmann, A. G.
- Venue
- Proceedings of the 15th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2007-09-29
- Pages
- 188-197
- OpenAlex
- W1978920452
- Cited by
- 667
- References
- 17
- FWCI
- 11.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yang, J., Yan, R., & Hauptmann, A. G. (2007). Cross-domain video concept detection using adaptive svms. Proceedings of the 15th ACM international conference on Multimedia. https://doi.org/10.1145/1291233.1291276
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