Semantic autoencoder for zero-shot learning

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 4447-4456

  Kodirov, Elyor ^a   Xiang, Tao ^a   Gong, Shaogang ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DECODING; LEARNING SYSTEMS; PATTERN RECOGNITION; SEMANTICS; SIGNAL ENCODING; VECTOR SPACES;

BENCHMARK DATASETS; COMPUTATIONAL COSTS; EFFICIENT LEARNING; PROJECTION FUNCTION; SEMANTIC EMBEDDING; SEMANTIC REPRESENTATION; STATE OF THE ART; SUPERVISED CLUSTERING;

LEARNING ALGORITHMS;

EID: 85044294853     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.473     Document Type: Conference Paper

References (67)

1. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. Labelembedding for attribute-based classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 819-826, 2013.
2. Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele. Evaluation of output embeddings for fine-grained image classifi-cation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2927-2936, 2015.
3. F. Ali, E. Ian, H. Derek, and D. Forsyth. Describing objects by their attributes. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1778-1785. IEEE, 2009.
4. V. Badrinarayanan, A. Kendall, and R. Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561, 2015.
5. P. Baldi and K. Hornik. Neural networks and principal component analysis: Learning from examples without local minima. Neural networks, 2(1):53-58, 1989.
6. R. BARTELS. Solution of the matrix equation ax+ xb= c [f4].commun. ACM, 15:820-826, 1972.
7. Y. Bengio. Learning deep architectures for ai. Foundations and trends® in Machine Learning, 2(1):1-127, 2009.
8. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, et al. Greedy layer-wise training of deep networks. 2007.
9. I. Biederman. Recognition-by-components: a theory of human image understanding. Psychological Review, 94(2):115, 1987.
10. Y.-l. Boureau, Y. L. Cun, et al. Sparse feature learning for deep belief networks. In Advances in neural information processing systems, pages 1185-1192, 2008.
11. M. Bucher, S. Herbin, and F. Jurie. Improving semantic embedding consistency by metric learning for zero-shot classiffication. In European Conference on Computer Vision, pages 730-746. Springer, 2016.
12. W. Catherine, B. Steve, W. Peter, P. Pietro, and B. Serge. The caltech-ucsd birds-200-2011 dataset. In California Institute of Technology, 2011.
13. S. Changpinyo, W.-L. Chao, B. Gong, and F. Sha. Synthesized classifiers for zero-shot learning. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
14. W.-L. Chao, S. Changpinyo, B. Gong, and F. Sha. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In ECCV. Springer, 2016.
15. M. Chen, W. EDU, and Z. E. Xu. Marginalized denoising autoencoders for domain adaptation. In ICML, 2014.
16. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. Information-theoretic metric learning. In Proceedings of the 24th international conference on Machine learning, pages 209-216. ACM, 2007.
17. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248-255. IEEE, 2009.
18. M. Elhoseiny, B. Saleh, and A. Elgammal. Write a classifier: Zero-shot learning using purely textual descriptions. In ICCV, 2013.
19. A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, T. Mikolov, et al. Devise: A deep visual-semantic embedding model. In Advances in neural information processing systems, pages 2121-2129, 2013.
20. Y. Fu, T. M. Hospedales, T. Xiang, Z. Fu, and S. Gong. Transductive multi-view embedding for zero-shot recognition and annotation. In ECCV, pages 584-599, 2014.
21. Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong. Transductive multi-view zero-shot learning. IEEE Tran. PAMI, 37(11):2332-2345, 2015.
22. Y. Fu and L. Sigal. Semi-supervised vocabulary-informed learning. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
23. Z. Fu, T. Xiang, E. Kodirov, and S. Gong. Zero-shot object recognition by semantic manifold distance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2635-2644, 2015.
24. P. Genevieve, X. Chen, S. Hang, and H. James. The sun attribute database: Beyond categories for deeper scene understanding. International Journal of Computer Vision, 108(1-2):59-81, 2014.
25. L. C. H, N. Hannes, and H. Stefan. Attribute-based classification for zero-shot visual object categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3):453-465, 2014.
26. D. Jayaraman and K. Grauman. Zero-shot recognition with unreliable attributes. In Advances in Neural Information Processing Systems, pages 3464-3472, 2014.
27. K. Kavukcuoglu, R. Fergus, Y. LeCun, et al. Learning invariant features through topographic filter maps. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1605-1612. IEEE, 2009.
28. E. Kodirov, T. Xiang, Z. Fu, and S. Gong. Unsupervised domain adaptation for zero-shot learning. In ICCV, pages 2452-2460, 2015.
29. E. Kodirov, T. Xiang, Z. Fu, and S. Gong. Learning robust graph regularisation for subspace clustering. In British Machine Vision Conference, 2016.
30. M. Köstinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof. Large scale metric learning from equivalence constraints. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2288-2295. IEEE, 2012.
31. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
32. R. Lajugie. Large-margin metric learning for constrained partitioning problems. In ICML, 2014.
33. M. T. Law, Y. Yu, M. Cord, and E. P. Xing. Closed-form training of mahalanobis distance for supervised clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
34. H. Lee, P. Pham, Y. Largman, and A. Y. Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In Advances in neural information processing systems, pages 1096-1104, 2009.
35. J. Lei Ba, K. Swersky, S. Fidler, et al. Predicting deep zeroshot convolutional neural networks using textual descriptions. In ICCV, pages 4247-4255, 2015.
36. Y. Lu. Unsupervised learning on neural network outputs: with application in zero-shot learning. arXiv preprint arXiv:1506.00990, 2015.
37. T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
38. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119, 2013.
39. M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1447-1454. IEEE, 2006.
40. M. Norouzi, T. Mikolov, S. Bengio, Y. Singer, J. Shlens, A. Frome, G. S. Corrado, and J. Dean. Zero-shot learning by convex combination of semantic embeddings. In ICLR, pages 488-501, 2014.
41. M. Perrot and A. Habrard. Regressive virtual metric learning. In Advances in Neural Information Processing Systems, pages 1810-1818, 2015.
42. M. Radovanović, A. Nanopoulos, and M. Ivanović. Hubs in space: Popular nearest neighbors in high-dimensional data. JMLR, 11(9):2487-2531, 2010.
43. M. Ranzato, Y. Boureau, S. Chopra, and Y. Lecun. A unified energy-based framework for unsupervised learning. 2007.
44. S. Reed, Z. Akata, B. Schiele, and H. Lee. Learning deep representations of fine-grained visual descriptions. In CVPR, 2016.
45. S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee. Generative adversarial text-to-image synthesis. In Proceedings of The 33rd International Conference on Machine Learning, 2016.
46. S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio. Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 833-840, 2011.
47. M. Rohrbach, S. Ebert, and B. Schiele. Transfer learning in a transductive setting. In Advances in neural information processing systems, pages 46-54, 2013.
48. M. Rohrbach, M. Stark, and B. Schiele. Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1641-1648. IEEE, 2011.
49. B. Romera-Paredes and P. H. Torr. An embarrassingly simple approach to zero-shot learning. Proceedings of The 32nd International Conference on Machine Learning (ICML), 2015.
50. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1. chapter Learning Internal Representations by Error Propagation, pages 318-362. MIT Press, Cambridge, MA, USA, 1986.
51. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Cognitive modeling, 5(3):1, 2012.
52. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.
53. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
54. Y. Shigeto, I. Suzuki, K. Hara, M. Shimbo, and Y. Matsumoto. Ridge regression, hubness, and zero-shot learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 135-151. Springer, 2015.
55. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
56. R. Socher, M. Ganjoo, C. D. Manning, and A. Ng. Zero-shot learning through cross-modal transfer. In Advances in neural information processing systems, pages 935-943, 2013.
57. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.
58. M. Thomas, V. Jakob, P. Florent, and C. Gabriela. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In Computer Vision-ECCV 2012, pages 488-501. Springer, 2012.
59. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096-1103. ACM, 2008.
60. U. Von Luxburg. A tutorial on spectral clustering. Statistics and computing, 17(4):395-416, 2007.
61. K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10(Feb):207-244, 2009.
62. J. Xie, R. Girshick, and A. Farhadi. Unsupervised deep embedding for clustering analysis. ICML, 2015.
63. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. In NIPS, 2003.
64. X. Yan, J. Yang, K. Sohn, and H. Lee. Attribute2image: Conditional image generation from visual attributes. In ECCV, 2016.
65. Y. Yang and T. Hospedales. A unified perspective on multidomain and multi-task learning. 2015.
66. Z. Zhang and V. Saligrama. Zero-shot learning via semantic similarity embedding. In Proceedings of the IEEE International Conference on Computer Vision, pages 4166-4174, 2015.
67. Z. Zhang and V. Saligrama. Zero-shot learning via joint latent similarity embedding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6034-6042, 2016.