Zero-shot learning through cross-modal transfer

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Socher, Richard ^a   Ganjoo, Milind ^a   Manning, Christopher D ^a   Ng, Andrew Y ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTIONS OF WORDS; LEARNING MODELS; NOVELTY DETECTION; SEMANTIC SPACE; STATE-OF-THE-ART PERFORMANCE; TRAINING DATA; TRAINING IMAGE; VISUAL FEATURE;

SEMANTICS;

EID: 84898938559     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. M. Baroni and A. Lenci. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics, 36(4):673-721, 2010.
2. E. Bart and S. Ullman. Cross-generalization: learning novel classes from a single example by feature replacement. In CVPR, 2005.
3. Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. A neural probabilistic language model. J. Mach. Learn. Res., 3, March 2003.
4. J. Blitzer, M. Dredze, and F. Pereira. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In ACL, 2007.
5. E. Bruni, G. Boleda, M. Baroni, and N. Tran. Distributional semantics in technicolor. In ACL, 2012.
6. A. Coates and A. Ng. The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization . In ICML, 2011.
7. R. Collobert and J. Weston. A unified architecture for natural language processing: deep neural networks with multitask learning. In ICML, 2008.
8. J. Curran. From Distributional to Semantic Similarity. PhD thesis, University of Edinburgh, 2004.
9. K. Erk and S. Padó. A structured vector space model for word meaning in context. In EMNLP, 2008.
10. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, 2009.
11. Y. Feng and M. Lapata. Visual information in semantic representation. In HLT-NAACL, 2010.
12. M. Fink. Object classification from a single example utilizing class relevance pseudo-metrics. In NIPS, 2004.
13. X. Glorot, A. Bordes, and Y. Bengio. Domain adaptation for Large-Scale sentiment classification: A deep learning approach. In ICML, 2011.
14. D. Hoiem, A.A. Efros, and M. Herbert. Geometric context from a single image. In ICCV, 2005.
15. E. H. Huang, R. Socher, C. D. Manning, and A. Y. Ng. Improving Word Representations via Global Context and Multiple Word Prototypes. In ACL, 2012.
16. Yangqing Jia, Chang Huang, and T. Darrell. Beyond spatial pyramids: Receptive field learning for pooled image features. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3370 -3377, june 2012.
17. H. Kriegel, P. Kröger, E. Schubert, and A. Zimek. LoOP: Local Outlier Probabilities. In Proceedings of the 18th ACM conference on Information and knowledge management, CIKM '09, 2009.
18. Alex Krizhevsky. Learning Multiple Layers of Features from Tiny Images. Master's thesis, Computer Science Department, University of Toronto, 2009.
19. R.; Perona L. Fei-Fei; Fergus. One-shot learning of object categories. TPAMI, 28, 2006.
20. B. M. Lake, J. Gross R. Salakhutdinov, and J. B. Tenenbaum. One shot learning of simple visual concepts. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 2011.
21. C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer. In CVPR, 2009.
22. T. K. Landauer and S. T. Dumais. A solution to Plato's problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2):211-240, 1997.
23. C.W. Leong and R. Mihalcea. Going beyond text: A hybrid image-text approach for measuring word relatedness. In IJCNLP, 2011.
24. D. Lin. Automatic retrieval and clustering of similar words. In Proceedings of COLING-ACL, pages 768-774, 1998.
25. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A.Y. Ng. Multimodal deep learning. In ICML, 2011.
26. S. Pado and M. Lapata. Dependency-based construction of semantic space models. Computational Linguistics, 33(2):161-199, 2007.
27. M. Palatucci, D. Pomerleau, G. Hinton, and T. Mitchell. Zero-shot learning with semantic output codes. In NIPS, 2009.
28. Guo-Jun Qi, C. Aggarwal, Y. Rui, Q. Tian, S. Chang, and T. Huang. Towards cross-category knowledge propagation for learning visual concepts. In CVPR, 2011.
29. A. Torralba R. Salakhutdinov, J. Tenenbaum. Learning to learn with compound hierarchical-deep models. In NIPS, 2012.
30. H. Schütze. Automatic word sense discrimination. Computational Linguistics, 24:97-124, 1998.
31. R. Socher and L. Fei-Fei. Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. In CVPR, 2010.
32. P. D. Turney and P. Pantel. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37:141-188, 2010.
33. L. van der Maaten and G. Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008.