Transductive multi-view embedding for zero-shot recognition and annotation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8690 LNCS, Issue PART 2, 2014, Pages 584-599

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

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

BENCHMARK DATASETS; INHERENT LIMITATIONS; LEARNING APPROACH; LOW-LEVEL FEATURES; PROJECTION DOMAIN; PROJECTION FUNCTION; SEMANTIC REPRESENTATION; VISUAL ATTRIBUTES;

SEMANTICS;

EID: 84906511785     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10605-2_38     Document Type: Conference Paper

References (38)

1. Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for attributebased classification. In: CVPR (2013)
2. Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S.: Clustering on the unit hypersphere using von mises-fisher distributions. JMLR (2005)
3. Biederman, I.: Recognition by components - a theory of human image understanding. Psychological Review (1987)
4. Blitzer, J., Foster, D.P., Kakade, S.M.: Zero-shot domain adaptation: A multi-view approach (2009)
5. Brown, P.F., Pietra, V.J.: V.deSouza, P., C.Lai, J., L.Mercer, R.: Class-based ngram models of natural language. Journal Computational Linguistics (1992)
6. Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)
7. Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., Mikolov, T.: Devise: A deep visual-semantic embedding model andrea. In: NIPS (2013)
8. Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Attribute learning for understanding unstructured social activity. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 530-543. Springer, Heidelberg (2012)
9. Fu, Y.: Multi-view metric learning for multi-view video summarization (2014), http://arxiv.org/abs/1405.6434
10. Fu, Y., Guo, Y., Zhu, Y., Liu, F., Song, C., Zhou, Z.H.: Multi-view video summarization. IEEE TMM 12(7), 717-729 (2010)
11. Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Learning multi-modal latent attributes. TPAMI (2013)
12. Fu, Y., Hospedales, T.M., Xiang, T., Yao, Y., Gong, S.: Interestingness prediction by robust learning to rank. In: ECCV (2014)
13. Gong, Y., Ke, Q., Isard, M., Lazebnik, S.: A multi-view embedding space for modeling internet images, tags, and their semantics. IJCV (2013)
14. Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis; an overview with application to learning methods. In: Neural Computation (2004)
15. Hospedales, T., Gong, S., Xiang, T.: Learning tags from unsegmented videos of multiple human actions. In: ICDM (2011)
16. Hwang, S.J., Grauman, K.: Learning the relative importance of objects from tagged images for retrieval and cross-modal search. IJCV (2011)
17. Hwang, S.J., Sha, F., Grauman, K.: Sharing features between objects and their attributes. In: CVPR (2011)
18. Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)
19. Lampert, C.H.: Kernel methods in computer vision. Foundations and Trends in Computer Graphics and Vision (2009)
20. Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zeroshot visual object categorization. IEEE TPAMI (2013)
21. Liu, J., Kuipers, B., Savarese, S.: Recognizing human actions by attributes. In: CVPR (2011)
22. van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-sne. JMLR (2008)
23. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representation in vector space. In: Proceedings of Workshop at ICLR (2013)
24. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS (2013)
25. Palatucci, M., Hinton, G., Pomerleau, D., Mitchell, T.M.: Zero-shot learning with semantic output codes. In: NIPS (2009)
26. Parikh, D., Grauman, K.: Relative attributes. In: ICCV (2011)
27. Rohrbach, M., Ebert, S., Schiele, B.: Transfer learning in a transductive setting. In: NIPS (2013)
28. Rohrbach, M., Stark, M., Schiele, B.: Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In: CVPR (2012)
29. Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., Schiele, B.: What helps where- and why semantic relatedness for knowledge transfer. In: CVPR (2010)
30. Scheirer, W.J., Kumar, N., Belhumeur, P.N., Boult, T.E.: Multi-attribute spaces: Calibration for attribute fusion and similarity search. In: CVPR (2012)
31. Shi, Z., Hospedales, T.M., Xiang, T.:Weakly supervised object-attribute prediction and localisation. In: ECCV (2014)
32. Smola, A.J., Kondor, R.: Kernels and regularization on graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 144-158. Springer, Heidelberg (2003)
33. Socher, R., Fei-Fei, L.: Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. In: CVPR (2010)
34. Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C.D., Ng, A.Y.: Zeroshot learning through cross-modal transfer. In: NIPS (2013)
35. Wang, X., Ji, Q.: A unified probabilistic approach modeling relationships between attributes and objects. In: ICCV (2013)
36. Wang, Y., Gong, S.: Translating topics to words for image annotation. In: ACM CIKM (2007)
37. Yu, F.X., Cao, L., Feris, R.S., Smith, J.R., Chang, S.F.: Designing category-level attributes for discriminative visual recognition. In: CVPR (2013)
38. Zhou, D., Burges, C.J.C.: Spectral clustering and transductive learning with multiple views. In: ICML 2007 (2007)