Computer Vision: Publications

Book chapters

• [1]

  A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

  Peter Carbonetto‚ Gyuri Dorkó‚ Cordelia Schmid‚ Hendrik Kück and Nando Freitas

  In Jean Ponce‚ Martial Hebert‚ Cordelia Schmid and Andrew Zisserman, editors, Toward Category−Level Object Recognition. Vol. 4170 of Lecture Notes in Computer Science. Pages 277−300. Springer Berlin Heidelberg. 2006.

  Details about A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues | BibTeX data for A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues | DOI (10.1007/11957959_15) | Link to A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

Journal papers

• [1]

  Matching words and pictures

  Kobus Barnard‚ Pinar Duygulu‚ David Forsyth‚ Nando de Freitas‚ David M. Blei and Michael I. Jordan

  In Journal of Machine Learning Research. Vol. 3. Pages 1107–1135. March, 2003.

  Details about Matching words and pictures | BibTeX data for Matching words and pictures | Link to Matching words and pictures

Conference papers

• [1]

  Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search

  Julieta Martinez‚ James Little and Nando de Freitas

  In IEEE Winter Conference on Applications of Computer Vision (WACV). 2014.

  Details about Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search | BibTeX data for Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search | Download (pdf) of Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search

• [2]

  Predicting Parameters in Deep Learning

  Misha Denil‚ Babak Shakibi‚ Laurent Dinh‚ Marc'Aurelio Ranzato and Nando de Freitas

  In Advances in Neural Information Processing Systems (NIPS). 2013.

  Details about Predicting Parameters in Deep Learning | BibTeX data for Predicting Parameters in Deep Learning | Download (pdf) of Predicting Parameters in Deep Learning

• [3]

  Learning about individuals from group statistics

  Hendrik Kuck and Nando de Freitas

  In Uncertainty in Artificial Intelligence (UAI). Pages 332–339. Arlington‚ Virginia. 2005. AUAI Press.

  Details about Learning about individuals from group statistics | BibTeX data for Learning about individuals from group statistics | Download (pdf) of Learning about individuals from group statistics

• [4]

  A Statistical Model for General Contextual Object Recognition

  Peter Carbonetto‚ Nando Freitas and Kobus Barnard

  In Tomas Pajdla and Jiri Matas, editors, European Conference on Computer Vision (ECCV). Vol. 3021 of Lecture Notes in Computer Science. Pages 350–362. Springer Berlin Heidelberg. 2004.

  Details about A Statistical Model for General Contextual Object Recognition | BibTeX data for A Statistical Model for General Contextual Object Recognition | DOI (10.1007/978-3-540-24670-1_27) | Link to A Statistical Model for General Contextual Object Recognition

• [5]

  The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval

  Eric Brochu‚ Nando de Freitas and Kejie Bao

  In Artificial Intelligence and Statistics (AISTATS). 2003.

  Details about The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval | BibTeX data for The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval | Download (pdf) of The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval

• [6]

  Why can't read?: the problem of learning semantic associations in a robot environment

  Peter Carbonetto and Nando de Freitas

  In Proceedings of the HLT−NAACL 2003 workshop on Learning word meaning from non−linguistic data. Pages 54–61. Stroudsburg‚ PA‚ USA. 2003. Association for Computational Linguistics.

  Details about Why can't read?: the problem of learning semantic associations in a robot environment | BibTeX data for Why can't read?: the problem of learning semantic associations in a robot environment | DOI (10.3115/1119212.1119220) | Link to Why can't read?: the problem of learning semantic associations in a robot environment

• [7]

  Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition

  Peter Carbonetto‚ Nando de Freitas‚ Paul Gustafson and Natalie Thompson

  In Artificial Intelligence and Statistics (AISTATS). 2003.

  Details about Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition | BibTeX data for Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition | Download (pdf) of Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition

• [8]

  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

  P. Duygulu‚ K. Barnard‚ J.F.G. Freitas and D.A. Forsyth

  In Anders Heyden‚ Gunnar Sparr‚ Mads Nielsen and Peter Johansen, editors, European Conference on Computer Vision (ECCV). Vol. 2353 of Lecture Notes in Computer Science. Pages 97−112. Springer Berlin Heidelberg. 2002.

  Best Paper prize in Cognitive Vision

  Details about Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary | BibTeX data for Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary | DOI (10.1007/3-540-47979-1_7) | Link to Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary