Gaussian visual-linguistic embedding for zero-shot recognition

EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Volumn , Issue , 2016, Pages 912-918

  Mukherjee, Tanmoy ^a   Hospedales, Timothy ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDINGS; SEMANTICS;

GAUSSIANS; SEMANTIC MODEL; SINGLE VECTORS; VISUAL RECOGNITION; WORD VECTORS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 85049401332     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d16-1089     Document Type: Conference Paper

References (30)

1. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. 2013. Label-embedding for attribute-based classification. In Computer Vision and Pattern Recognition.
2. Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2015. Random walks on context spaces: Towards an explanation of the mysteries of semantic word embeddings. CoRR, abs/1502.03520.
3. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. JMLR, 3:993-1022.
4. Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012a. Distributional semantics in technicolor. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1, pages 136-145.
5. Elia Bruni, Jasper Uijlings, Marco Baroni, and Nicu Sebe. 2012b. Distributional semantics with eyes: Using image analysis to improve computational representations of word meaning. In ACM Multimedia.
6. Elia Bruni, Nam Khanh Tran, and Marco Baroni. 2014. Multimodal distributional semantics. J. Artif. Int. Res., 49(1):1-47, January.
7. Xinchi Chen, Xipeng Qiu, Jingxiang Jiang, and Xuanjing Huang. 2015. Gaussian mixture embeddings for multiple word prototypes. arXiv preprint arXiv:1511.06246.
8. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. J. Mach. Learn. Res., 12:2493-2537, November.
9. Georgiana Dinu, Angeliki Lazaridou, and Marco Baroni. 2015. Improving zero-shot learning by mitigating the hubness problem. In ICLR Workshop Paper.
10. Adriano Ferraresi, Eros Zanchetta, Marco Baroni, and Silvia Bernardini. 2008. Introducing and evaluating ukwac, a very large web-derived corpus of english. In In Proceedings of the 4th Web as Corpus Workshop (WAC-4.
11. Andrea Frome, Greg Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, MarcAurelio Ranzato, and Tomas Mikolov. 2013. Devise: A deep visual-semantic embedding model. In Neural Information Processing Systems (NIPS).
12. Yanwei Fu, Timothy Hospedales, Tony Xiang, Zhenyong Fu, and Shaogang Gong. 2014. Transductive multi-view embedding for zero-shot recognition and annotation. In European Conference on Computer Vision.
13. Z. Fu, T. A. Xiang, E. Kodirov, and S. Gong. 2015. Zero-shot object recognition by semantic manifold distance. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2635-2644, June.
14. Zellig Harris. 1954. Distributional structure. Word, 10(23):146-162.
15. T. Jebara, R. Kondor, and A. Howard. 2004. Probability product kernels. Journal of Machine Learning Research, 5:819-844.
16. Douwe Kiela and Léon Bottou. 2014. Learning image embeddings using convolutional neural networks for improved multi-modal semantics. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-14).
17. Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling. 2009. Learning to detect unseen object classes by between-class attribute transfer. In Computer Vision and Pattern Recognition.
18. Angeliki Lazaridou, Elia Bruni, and Marco Baroni. 2014. Is this a wampimuk? cross-modal mapping between distributional semantics and the visual world. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), June.
19. Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. 2012. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In European Conference on Computer Vision.
20. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, pages 3111 -3119.
21. Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, and Jeffrey Dean. 2014. Zero-shot learning by convex combination of semantic embeddings. In ICLR.
22. Scott Reed, Zeynep Akata, Honglak Lee, and Bernt Schiele. 2016. Learning deep representations of fine-grained visual descriptions. In IEEE Computer Vision and Pattern Recognition (CVPR).
23. Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, and Alan Yuille. 2016. Joint image-text representation by gaussian visual semantic embedding. In Proceeding of ACM International Conference on Multimedia (ACM MM).
24. Bernardino Romera-Paredes and Philip H. S. Torr. 2015. An embarrassingly simple approach to zero-shot learning. In ICML.
25. Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, and David M. Blei. 2016. Exponential Family Embeddings, August.
26. Holger Schwenk. 2007. Continuous space language models. Computer Speech and Language, 21.
27. Carina Silberer and Mirella Lapata. 2014. Learning grounded meaning representations with autoencoders. In ACL.
28. Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations.
29. Richard Socher, Milind Ganjoo, Christopher D. Manning, and Andrew Y. Ng. 2013. Zero Shot Learning Through Cross-Modal Transfer. In Advances in Neural Information Processing Systems 26.
30. Luke Vilnis and Andrew McCallum. 2015. Word representations via gaussian embedding. In ICLR.