%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 04:47:03 PM

@INPROCEEDINGS{grangier:2006:icann,
         author = {Grangier, David and Bengio, Samy},
       projects = {Idiap},
          title = {A Neural Network to Retrieve Images from Text Queries},
      booktitle = {International Conference on Artificial Neural Networks {(ICANN)}},
         volume = {2},
           year = {2006},
       crossref = {grangier:2006:idiap-06-33},
       abstract = {This work presents a neural network for the retrieval of images from text queries. The proposed network is composed of two main modules: the first one extracts a global picture representation from local block descriptors while the second one aims at solving the retrieval problem from the extracted representation. Both modules are trained jointly to minimize a loss related to the retrieval performance. This approach is shown to be advantageous when compared to previous models relying on unsupervised feature extraction: average precision over Corel queries reaches 26.2\\% for our model, which should be compared to 21.6\\% for PAMIR, the best alternative.},
            pdf = {https://publications.idiap.ch/attachments/reports/2006/grangier_icann06.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2006/grangier_icann06.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{grangier:2006:idiap-06-33,
         author = {Grangier, David and Bengio, Samy},
       projects = {Idiap},
          title = {A Neural Network to Retrieve Images from Text Queries},
           type = {Idiap-RR},
         number = {Idiap-RR-33-2006},
           year = {2006},
    institution = {IDIAP},
       abstract = {This work presents a neural network for the retrieval of images from text queries. The proposed network is composed of two main modules: the first one extracts a global picture representation from local block descriptors while the second one aims at solving the retrieval problem from the extracted representation. Both modules are trained jointly to minimize a loss related to the retrieval performance. This approach is shown to be advantageous when compared to previous models relying on unsupervised feature extraction: average precision over Corel queries reaches 26.2\\% for our model, which should be compared to 21.6\\% for PAMIR, the best alternative.},
            pdf = {https://publications.idiap.ch/attachments/reports/2006/grangier_rr06-33.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2006/grangier_rr06-33.ps.gz},
ipdmembership={learning},
}