iccv iccv2013 iccv2013-123 iccv2013-123-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Fatemeh Mirrashed, Mohammad Rastegari
Abstract: We propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target domain. The key insight is finding attributes that are discriminative across categories and predictable across domains. We achieve a performance that significantly exceeds the state-of-the-art results on standard benchmarks. In fact, in many cases, our method reaches the same-domain performance, the upper bound, in unsupervised domain adaptation scenarios.
[1] R. G. ad R. Li and R. Chellappa. Domain adaptation for object recognition: An unsupervised approach. In ICCV, 2011. 1, 2, 4, 5, 6
[2] H. Bay, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. In In ECCV, 2006. 5
[3] A. Bergamo and L. Torresani. Exploiting weakly-labeled web images to improve object classification: A domain adaptation approach. In NIPS, 2010. 1
[4] J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In ACL, 2007. 1, 2, 4, 5
[5] J. Blitzer, R. McDonald, and F. Pereira. Domain adaptation with structural correspondence learning. In Conference on Empirical Methods in Natural Language Processing, 2006. 1, 2, 5, 6
[6] H. Daum e´, III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 2006. 2
[7] H. Daume III. Frustratingly easy domain adaptation. 2007. 1
[8] H. Daume III, A. Kumar, and A. Saha. Co-regularization based semisupervised domain adaptation. In NIPS, 2010. 1
[9] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88, 2010. 6
[10] R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 2008. 6
[11] A. H. G. Griffin and P. Perona. Caltech-256 object category dataset. In Technical report, 2007. 5, 6
[12] B. Gong, Y. Shi, K. Grauman, and F. Sha. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In ICML, 2013. 1, 2, 4, 5, 6
[13] B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, 2012. 1, 2, 4, 5, 6
[14] Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, 2011. 2
[15] H. J., A. Gretton, K. Borgwardt, and B. Scholkopf. Correcting sample selection bias by unlabeled data. In NIPS, 2007. 5, 6 The dashed part of the arrow indicates that the same hyperplane which
[16] A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba. Undoing the damage of dataset bias. In ECCV, 2012. 2, 4, 6, 7
[17] B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011. 4, 5
[18] D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. IJCV, 2, 2004. 6
[19] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 2011. 1, 2, 4, 5, 6
[20] M. Rastegari, A. Farhadi, and D. Forsyth. Attribute discovery via predictable discriminative binary codes. In ECCV, 2012. 1, 3, 4
[21] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: A database and web-based tool for image annotation. IJCV,
[22]
[23]
[24]
[25] 2007. 6 K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, 2010. 1, 2, 4, 5, 6 A. Torralba and A. Efros. Unbiased look at dataset bias. In CVPR, 2011. 2, 4, 6 A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http : / /www .vlfe at . org/ , 2008. 6 J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010. 6 2615