Transductive Multi-View Zero-Shot Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 37, Issue 11, 2015, Pages 2332-2345

  Fu, Yanwei ^a   Hospedales, Timothy M ^b   Xiang, Tao ^b   Gong, Shaogang ^b  

^a DISNEY RESEARCH   (United States)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

heterogeneous hypergraph; multi view Learning; Transducitve learning; transfer Learning; zero shot Learning

Indexed keywords

FEEDFORWARD NEURAL NETWORKS;

HYPERGRAPH; MULTI-VIEW LEARNING; TRANSDUCITVE LEARNING; TRANSFER LEARNING; ZERO-SHOT LEARNING;

SEMANTICS;

EID: 84941001216     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2015.2408354     Document Type: Article

References (57)

1. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid, "Labelembedding for attribute-based classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 819-826.
2. Y. Bengio, "Learning deep architectures for AI," Found. Trends Mach. Learn., vol. 2, pp. 1-127, 2009.
3. I. Biederman, "Recognition by components-A theory of human image understanding," Psychological Rev., vol. 94, pp. 115-147, 1987.
4. P. F. Brown, V. J. Pietra, P. V. de Souza, J. C. Lai, and R. L. Mercer, "Class-based n-gram models of natural language," J. Comput. Linguistics, vol. 18, pp. 467-479, 1992.
5. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, "Return of the devil in the details: Delving deep into convolutional nets," ArXiv e-prints, 2014.
6. X. Chen, A. Shrivastava, and A. Gupta, "NEIL: Extracting visual knowledge from web data," in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 1409-1416.
7. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, "DeCAF: A deep convolutional activation feature for generic visual recognition," CoRR, abs/1310.1531, 2013.
8. L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank, "Domain transfer SVM for video concept detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 1375-1381.
9. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, "Describing objects by their attributes," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 1778-1785.
10. B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, "Unsupervised visual domain adaptation using subspace alignment," in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 2960-2967.
11. A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov, "DeViSE: A deep visual-semantic embedding model," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 2121-2129.
12. Y. Fu, Y. Guo, Y. Zhu, F. Liu, C. Song, and Z.-H. Zhou, "Multiview video summarization," IEEE Trans. Multimedia, vol. 12, no. 7, pp. 717-729, Nov. 2010.
13. Y. Fu, T. Hospedales, T. Xiang, and S. Gong, "Attribute learning for understanding unstructured social activity," in Proc. 12th Eur. Conf. Comput. Vis., 2012, pp. 530-543.
14. Y. Fu, T. M. Hospedales, T. Xiang, Z. Fu, and S. Gong, "Transductive multi-view embedding for zero-shot recognition and annotation," Computer Vision-ECCV 2014 Lecture Notes in Computer Science, vol. 8690, pp. 584-599, 2014.
15. Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, "Learning multimodal latent attributes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 2, pp. 303-316, Feb. 2014.
16. Y. Fujiwara and G. Irie, "Efficient label propagation," in Proc. 31st Int. Conf. Mach. Learning, 2014, pp. 784-792.
17. Y. Gong, Q. Ke, M. Isard, and S. Lazebnik, "A multi-view embedding space for modeling internet images, tags, and their semantics," Int. J. Comput. Vis., vol. 106, pp. 210-233, 2014.
18. D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, "Canonical correlation analysis: An overview with application to learning methods," Neural Comput., vol. 16, pp. 2639-2664, 2004.
19. C. Hong, J. Yu, J. Li, and X. Chen, "Multi-view hypergraph learning by patch alignment framework," Neurocomputing, vol. 118, pp. 79-86, 2013.
20. T. Hospedales, S. Gong, and T. Xiang, "Learning tags from unsegmented videos of multiple human actions," in Proc. IEEE 11th Int. Conf. Data Mining, 2011, pp. 251-259.
21. Y. Huang, Q. Liu, and D. Metaxas, "Video object segmentation by hypergraph cut," in Proc. IEEE Conf. Comput. Vis. Patern Recog., 2009, pp. 1738-1745.
22. Y. Huang, Q. Liu, S. Zhang, and D. N. Metaxas, "Image retrieval via probabilistic hypergraph ranking," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 3376-3383.
23. S. J. Hwang and K. Grauman, "Learning the relative importance of objects from tagged images for retrieval and cross-modal search," Int. J. Comput. Vis., vol. 100, pp. 134-153, 2011.
24. S. J. Hwang, F. Sha, and K. Grauman, "Sharing features between objects and their attributes," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 1761-1768.
25. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097-1105.
26. C. H. Lampert, "Kernel methods in computer vision," Found. Trends Comput. Graph. Vis., vol. 4, pp. 193-285, 2009.
27. C. H. Lampert, H. Nickisch, and S. Harmeling, "Learning to detect unseen object classes by between-class attribute transfer," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 951-958.
28. C. H. Lampert, H. Nickisch, and S. Harmeling, "Attribute-based classification for zero-shot visual object categorization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 3, pp. 453-465, Mar. 2014.
29. X. Li, W. Hu, C. Shen, A. Dick, and Z. Zhang, "Context-aware hypergraph construction for robust spectral clustering," IEEE Trans. Knowl. Data Eng., vol. 26, no. 10, pp. 2588-2597, Oct. 2014.
30. X. Li, Y. Li, C. Shen, A. R. Dick, and A. van den Hengel, "Contextual hypergraph modelling for salient object detection," in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 3328-3335.
31. J. Liu, B. Kuipers, and S. Savarese, "Recognizing human actions by attributes," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 3337-3344.
32. T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representation in vector space," in Proc. Workshop ICLR, 2013.
33. M. Palatucci, G. Hinton, D. Pomerleau, and T. M. Mitchell, "Zeroshot learning with semantic output codes," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1410-1418.
34. D. Parikh and K. Grauman, "Relative attributes," in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 503-510.
35. A. Pentina and C. H. Lampert, "A PAC-Bayesian bound for lifelong learning anastasia," in Proc. Int. Conf. Mach. Learning, 2014, pp. 991-999.
36. M. Rohrbach, S. Ebert, and B. Schiele, "Transfer learning in a transductive setting," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 46-54.
37. M. Rohrbach, M. Stark, and B. Schiele, "Evaluating knowledge transfer and zero-shot learning in a large-scale setting," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 1641-1648.
38. M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele, "What helps where-and why semantic relatedness for knowledge transfer," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 910-917.
39. E. Rosch, "Classification of real-world objects: Origins and representations in cognition," in Thinking: Readings in Cognitive Science. Cambridge, U.K.: Cambridge Univ. Press, 1977.
40. R. Rosipal and N. Kramer, "Overview and recent advances in partial least squares," in Subspace, Latent Structure and Feature Selection, C. Saunders, M. Grobelnik, S. Gunn, and J. Shawe-Taylor, Eds. Berlin, Germany: Springer, 2006, pp. 34-51.
41. W. J. Scheirer, N. Kumar, P. N. Belhumeur, and T. E. Boult, "Multi-attribute spaces: Calibration for attribute fusion and similarity search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 2933-2940.
42. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le Cun, "Overfeat: Integrated recognition, localization and detection using convolutional networks," in Proc. Int. Conf. Learning Representations, 2014.
43. R. Socher and L. Fei-Fei, "Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 966-973.
44. R. Socher, M. Ganjoo, H. Sridhar, O. Bastani, C. D. Manning, and A. Y. Ng, "Zero-shot learning through cross-modal transfer," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 935-943.
45. A. J. Storkey and M. Sugiyama, "Mixture regression for covariate shift," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 1337-1344.
46. L. Sun, S. Ji, and J. Ye, "Hypergraph spectral learning for multilabel classification," in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 668-676.
47. L. van der Maaten and G. Hinton, "Visualizing high-dimensional data using t-SNE," J. Mach. Learning Res., vol. 9, pp. 2579-2605, 2008.
48. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, "The Caltech-UCSD Birds-200-2011 Dataset," California Inst. Technol., Pasadena, CA, USA, Tech. Rep. CNS-TR-2011-001, 2011.
49. K. Wang, R. He, W. Wang, L. Wang, and T. Tan, "Learning coupled feature spaces for cross-modal matching," in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 2088-2095.
50. X. Wang and Q. Ji, "A unified probabilistic approach modeling relationships between attributes and objects," in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 2120-2127.
51. Y. Wang and S. Gong, "Translating topics to words for image annotation," in Proc. 16th ACM Conf. Inf. Knowl. Manage., 2007, pp. 995-998.
52. D. Watts and S. Strogatz, "Collective dynamics of 'small-world' networks," Nature, vol. 393, pp. 440-442, 1998.
53. D. J. Watts, Small Worlds: The Dynamics of Networks between Order and Randomness, 8th ed. Oakland, CA, USA: Univ. Press California, 2004.
54. F. X. Yu, L. Cao, R. S. Feris, J. R. Smith, and S.-F. Chang, "Designing category-level attributes for discriminative visual recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 771-778.
55. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, "Learning with local and global consistency," in Proc. Adv. Inf. Process. Syst., 2004, pp. 321-328.
56. D. Zhou and C. J. C. Burges, "Spectral clustering and transductive learning with multiple views," in Proc. Int. Conf. Mach. Learning, 2007, pp. 1159-1166.
57. X. Zhu, "Semi-supervised learning literature survey," Dept. Comput. Sci., Univ. Wisconsin-Madison, Madison, WI, USA, Tech. Rep. 1530, 2007.