Abstract
Despite the increasing interest towards domain adaptation and transfer learning techniques to generalize over image collections and overcome their biases, the visual community misses a large scale testbed for cross-dataset analysis. In this paper we discuss the challenges faced when aligning twelve existing image databases in a unique corpus, and we propose two cross-dataset setups that introduce new interesting research questions. Moreover, we report on a first set of experimental domain adaptation tests showing the effectiveness of iterative self-labeling for large scale problems.
Chapter PDF
Similar content being viewed by others
References
Bergamo, A., Torresani, L.: Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. In: NIPS (2010)
Bruzzone, L., Marconcini, M.: Domain adaptation problems: A dasvm classification technique and a circular validation strategy. IEEE Trans. PAMI 32(5), 770–787 (2010)
Chen, M., Weinberger, K.Q., Blitzer, J.: Co-training for domain adaptation. In: NIPS (2011)
Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What Does Classifying More Than 10,000 Image Categories Tell Us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR (2009)
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531 (2013)
Everingham, M., Gool, L.V., Williams, C.K., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. IJCV 88(2) (2010)
Fang, C., Xu, Y., Rockmore, D.N.: Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias. In: ICCV (2013)
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: ICCV (2013)
Gong, B., Sha, F., Grauman, K.: Overcoming dataset bias: An unsupervised domain adaptation approach. In: NIPS Workshop on Large Scale Visual Recognition and Retrieval (2012)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR (2012)
Gong, B., Grauman, K., Sha, F.: Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In: ICML (2013)
Gong, B., Grauman, K., Sha, F.: Reshaping visual datasets for domain adaptation. In: NIPS (2013)
Griffin, G., Holub, A., Perona, P.: Caltech 256 object category dataset. Tech. Rep. UCB/CSD-04-1366, California Institue of Technology (2007)
Hand, D.J.: Classifier Technology and the Illusion of Progress. Stat. Sci. 21, 1–15 (2006)
Hand, D.J.: Academic obsessions and classification realities: ignoring practicalities in supervised classification. In: Classification, Clustering, and Data Mining Applications, pp. 209–232 (2004)
Hoffman, J., Kulis, B., Darrell, T., Saenko, K.: Discovering Latent Domains for Multisource Domain Adaptation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 702–715. Springer, Heidelberg (2012)
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the Damage of Dataset Bias. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 158–171. Springer, Heidelberg (2012)
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. In: ICRA (2011)
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between class attribute transfer. In: CVPR (2009)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: CVPR (2003)
Microsoft: Microsoft Research Cambridge Object Recognition Image Database. http://research.microsoft.com/en-us/downloads/b94de342-60dc-45d0-830b-9f6eff91b301/default.aspx (2005)
Ordonez, V., Deng, J., Choi, Y., Berg, A.C., Berg, T.L.: From large scale image categorization to entry-level categories. In: ICCV (2013)
Patricia, N., Caputo, B.: Learning to learn, from transfer learning to domain adaptation: A unifying perspective. In: CVPR (2014)
Qiu, Q., Patel, V.M., Turaga, P., Chellappa, R.: Domain Adaptive Dictionary Learning. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 631–645. Springer, Heidelberg (2012)
Rodner, E., Hoffman, J., Donahue, J., Darrell, T., Saenko, K.: Towards adapting imagenet to reality: Scalable domain adaptation with implicit low-rank transformations. CoRR abs/1308.4200 (2013)
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)
Sanchez, J., Perronnin, F.: High-dimensional signature compression for large-scale image classification. In: CVPR (2011)
Tommasi, T., Caputo, B.: Frustratingly easy nbnn domain adaptation. In: ICCV (2013)
Tommasi, T., Quadrianto, N., Caputo, B., Lampert, C.H.: Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 1–15. Springer, Heidelberg (2013)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR (2011)
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: Large-scale scene recognition from abbey to zoo. In: CVPR (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tommasi, T., Tuytelaars, T. (2015). A Testbed for Cross-Dataset Analysis. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8927. Springer, Cham. https://doi.org/10.1007/978-3-319-16199-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-16199-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16198-3
Online ISBN: 978-3-319-16199-0
eBook Packages: Computer ScienceComputer Science (R0)