The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Undoing the Damage of Dataset Bias
Open accessAbstract
The presence of bias in existing object recognition datasets is now well-known in the computer vision community. While it remains in question whether creating an unbiased dataset is possible given limited resources, in this work we propose a discriminative framework that directly exploits dataset bias during training. In particular, our model learns two sets of weights: (1) bias vectors associated with each individual dataset, and (2) visual world weights that are common to all datasets, which are learned by undoing the associated bias from each dataset. The visual world weights are expected to be our best possible approximation to the object model trained on an unbiased dataset, and thus tend to have good generalization ability. We demonstrate the effectiveness of our model by applying the learned weights to a novel, unseen dataset, and report superior results for both classification and detection tasks compared to a classical SVM that does not account for the presence of bias. Overall, we find that it is beneficial to explicitly account for bias when combining multiple datasets.
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Cites
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The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
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
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
Locality-constrained Linear Coding for image classification
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Adapting visual category models to new domains
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Self-citationDataset Shift in Machine Learning
(2008) · doi:10.7551/mitpress/9780262170055.001.0001 · Cited by 1,553
Cited by
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150
Deep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315
Unsupervised Visual Domain Adaptation Using Subspace Alignment
Fernando, B., Habrard, A., Sebban, M., & Tuytelaars, T. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.368 · Cited by 1,314
Learning to Generalize: Meta-Learning for Domain Generalization
Li, D., Yang, Y., Song, Y.-Z., & Hospedales, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11596 · Cited by 1,251
Metadata
- Authors
- Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., & Torralba, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 158-171
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1852255964
- Cited by
- 480
- References
- 10
- FWCI
- 15.27
- Type
- conference paper
- Language
- en
- Open access
- green
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- Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., & Torralba, A. (2012). Undoing the Damage of Dataset Bias. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-33718-5_12
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