An embarrassingly simple approach to zero-shot learning

32nd International Conference on Machine Learning, ICML 2015

Volumn 3, Issue , 2015, Pages 2142-2151

  Romera Paredes, Bernardino ^a   Torr, Philip H S ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; NETWORK LAYERS;

DOMAIN ADAPTATION; GENERALISATION; LEARNING APPROACH; LINE OF CODES; REAL DATA SETS; SIMPLE APPROACH; STATE-OF-THE-ART APPROACH; TOP LAYERS;

LEARNING SYSTEMS;

EID: 84969931523     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Akata, Zeynep, Perronnin, Florent, Harchaoui, Zaid, and Schmid, Cordelia. Label-embedding for attribute-based classification. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pp. 819-826. IEEE, 2013.
2. Argyriou, Andreas, Evgeniou, Theodoros, and Pontil, Massimiliano. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
3. Argyriou, Andreas, Micchelli, Charles A, and Pontil, Massimiliano. When is there a representer theorem? vector versus matrix regularizers. The Journal of Machine Learning Research, 10:2507-2529, 2009.
4. Ben-David, Shai, Blitzer, John, Crammer, Koby, Pereira, Fernando, et al. Analysis of representations for domain adaptation. Advances in neural information processing systems, 19:137, 2007.
5. Blitzer, John, Crammer, Koby, Kulesza, Alex, Pereira, Fernando, and Wortman, Jennifer. Learning bounds for domain adaptation. In Advances in neural information processing systems, pp. 129-136, 2008.
6. Bosch, Anna, Zisserman, Andrew, and Munoz, Xavier. Representing shape with a spatial pyramid kernel. In Proceedings of the 6th ACM international conference on Image and video retrieval, pp. 401-408. ACM, 2007.
7. Croonenborghs, Tom, Driessens, Kurt, and Bruynooghe, Maurice. Learning relational options for inductive transfer in relational reinforcement learning. In Inductive Logic Programming, pp. 88-97. Springer, 2008.
8. Daumé III, Hal. Frustratingly easy domain adaptation. arXiv preprint arXiv:0907.1815, 2009.
9. Dietterich, Thomas G. and Bakiri, Ghulum. Solving multiclass learning problems via error-correcting output codes. arXiv preprint cs/9501101, 1995.
10. Farhadi, Ali, Endres, Ian, Hoiem, Derek, and Forsyth, David. Describing objects by their attributes. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 1778-1785. IEEE, 2009.
11. Ferrari, Vittorio and Zisserman, Andrew. Learning visual attributes. In Advances in Neural Information Processing Systems, pp. 433-440, 2007.
12. Frome, Andrea, Corrado, Greg S, Shlens, Jon, Bengio, Samy, Dean, Jeff, Mikolov, Tomas, et al. Devise: A deep visual-semantic embedding model. In Advances in Neural Information Processing Systems, pp. 2121-2129, 2013.
13. Fu, Yanwei, Hospedales, Timothy M, Xiang, Tao, and Gong, Shaogang. Learning multimodal latent attributes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(2):303-316, 2014.
14. Harihafan, Bharath, Vishwanathan, SVN, and Varma, Manik. Efficient max-margin multi-label classification with applications to zero-shot learning. Machine learning, 88(1-2): 127-155, 2012.
15. Hwang, Sung Ju, Sha, Fei, and Grauman, Kristen. Sharing features between objects and their attributes. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 1761-1768. IEEE, 2011.
16. Jayaraman, Dinesh and Grauman, Kristen. Zero-shot recognition with unreliable attributes, pp. 3464-3472, 2014.
17. Jayaraman, Dinesh, Sha, Fei, and Grauman, Kristen. Decorrelating semantic visual attributes by resisting the urge to share. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp. 1629-1636. IEEE, 2014.
18. Jiang, Jing and Zhai, ChengXiang. Instance weighting for domain adaptation in nip. In ACL, volume 7, pp. 264-271, 2007.
19. Kifer, Daniel, Ben-David, Shai, and Gehrke, Johannes. Detecting change in data streams. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pp. 180-191. VLDB Endowment, 2004.
20. Lampert, Christoph H, Nickisch, Hannes, and Harmeling, Stefan. Learning to detect unseen object classes by between-class attribute transfer. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 951-958. IEEE, 2009.
21. Lampert, Christoph H, Nickisch, Hannes, and Harmeling, Stefan. Attribute-based classification for zero-shot visual object categorization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(3):453-465, 2014.
22. Lawrence, Neil D and Piatt, John C. Learning to learn with the informative vector machine. In Proceedings of the twenty-first international conference on Machine learning, pp. 65. ACM, 2004.
23. Liu, Jingen, Kuipers, Benjamin, and Savarese, Silvio. Recognizing human actions by attributes. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 3337-3344. IEEE, 2011.
24. Lowe, David G. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004.
25. Mahajan, Dhruv, Sellamanickam, Sundararajan, and Nair, Vinod. A joint learning framework for attribute models and object descriptions. In Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 1227-1234. IEEE, 2011.
26. Palatucci, Mark, Hinton, Geoffrey, Pomerleau, Dean, and Mitchell, Tom M. Zero-Shot Learning with Semantic Output Codes. Neural Information Processing Systems, pp. 1-9, 2009.
27. Pan, Sinno Jialin and Yang, Qiang. A survey on transfer learning. Knowledge and Data Engineering, IEEE Transactions on, 22(10):1345-1359, 2010.
28. Patterson, Genevieve and Hays, James. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2751-2758. IEEE, 2012.
29. Raykar, Vikas C, Krishnapuram, Balaji, Bi, Jinbo, Dundar, Murat, and Rao, R Bharat. Bayesian multiple instance learning: automatic feature selection and inductive transfer. In Proceedings of the 25th international conference on Machine learning, pp. 808-815. ACM, 2008.
30. Romera-Paredes, Bernardino, Aung, Hane, Bianchi-Berthouze, Nadia, and Pontil, Massimiliano. Multilinear multitask learning. In Proceedings of the 30th International Conference on Machine Learning, pp. 1444-1452, 2013.
31. Rückert, Ulrich and Kramer, Stefan. Kernel-based inductive transfer. In Machine Learning and Knowledge Discovery in Databases, pp. 220-233. Springer, 2008.
32. Suzuki, Masahiro, Sato, Haruhiko, Oyama, Satoshi, and Kurihara, Masahito. Transfer learning based on the observation probability of each attribute. In Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on, pp. 3627-3631. IEEE, 2014.
33. Wang, Yang and Mori, Greg. A discriminative latent model of object classes and attributes. In Computer Vision-ECCV2010, pp. 155-168. Springer, 2010.