Advertisement

Undoing the Damage of Dataset Bias

  • Aditya Khosla
  • Tinghui Zhou
  • Tomasz Malisiewicz
  • Alexei A. Efros
  • Antonio Torralba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

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.

Keywords

Target Domain Domain Adaptation Transfer Learning Visual World Spatial Pyramid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large data set for nonparametric object and scene recognition. PAMI 30(11), 1958–1970 (2008)CrossRefGoogle Scholar
  2. 2.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR (2009)Google Scholar
  3. 3.
    Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88, 303–338 (2010)CrossRefGoogle Scholar
  4. 4.
    Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR, pp. 1521–1528 (2011)Google Scholar
  5. 5.
    Ponce, J., Berg, T.L., Everingham, M., Forsyth, D., Hebert, M., Lazebnik, S., Marszalek, M., Schmid, C., Russell, B.C., Torralba, A., Williams, C.K.I., Zhang, J., Zisserman, A.: Dataset Issues in Object Recognition. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 29–48. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.: Dataset shift in machine learning. MIT Press (2009)Google Scholar
  7. 7.
    Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting Visual Category Models to New Domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In: CVPR (2011)Google Scholar
  9. 9.
    Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: ICCV (2011)Google Scholar
  10. 10.
    Jain, V., Learned-Miller, E.: Online domain adaptation of a pre-trained cascade of classifiers. In: CVPR (2011)Google Scholar
  11. 11.
    Evgeniou, T., Pontil, M.: Regularized multi–task learning. In: 10th ACM SIGKDD International Conf. Knowledge Discovery and Data Mining, pp. 109–117 (2004)Google Scholar
  12. 12.
    Ben-David, S., Schuller, R.: Exploiting Task Relatedness for Multiple Task Learning. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 567–580. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22 (2010)Google Scholar
  14. 14.
    Bergamo, A., Torresani, L., Fitzgibbon, A.: Picodes: Learning a compact code for novel-category recognition. In: NIPS (2011)Google Scholar
  15. 15.
    Perronnin, F., Sánchez, J., Liu, Y.: Large-scale image categorization with explicit data embedding. In: CVPR, pp. 2297–2304. IEEE (2010)Google Scholar
  16. 16.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  18. 18.
    Russell, B., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. In: IJCV (2007)Google Scholar
  19. 19.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: CVPR Workshop of Generative Model Based Vision (2004)Google Scholar
  20. 20.
    Choi, M.J., Lim, J.J., Torralba, A., Willsky, A.S.: Exploiting hierarchical context on a large database of object categories. In: CVPR, pp. 129–136 (2010)Google Scholar
  21. 21.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  22. 22.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)Google Scholar
  23. 23.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)Google Scholar
  24. 24.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. JMLR 9, 1871–1874 (2008)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aditya Khosla
    • 1
  • Tinghui Zhou
    • 2
  • Tomasz Malisiewicz
    • 1
  • Alexei A. Efros
    • 2
  • Antonio Torralba
    • 1
  1. 1.Massachusetts Institute of TechnologyUSA
  2. 2.Carnegie Mellon UniversityUSA

Personalised recommendations