Unlabeled data improves word prediction

Nicolas Loeff, Ali Farhadi, Ian Endres, David A. Forsyth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Labeling image collections is a tedious task, especially when multiple labels have to be chosen for each image. In this paper we introduce a new framework that extends state of the art models in word prediction to incorporate information from unlabeled examples, using manifold regularization. To the best of our knowledge this is the first semi-supervised multi-task model used in vision problems. The new model can be solved using gradient descent and is fast and efficient. We show remarkable improvements for cases with few labeled examples for challenging multi-task learning problems in vision (predicting words for images and attributes for objects).

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Pages956-962
Number of pages7
DOIs
StatePublished - Dec 1 2009
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: Sep 29 2009Oct 2 2009

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Other

Other12th International Conference on Computer Vision, ICCV 2009
CountryJapan
CityKyoto
Period9/29/0910/2/09

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Unlabeled data improves word prediction'. Together they form a unique fingerprint.

  • Cite this

    Loeff, N., Farhadi, A., Endres, I., & Forsyth, D. A. (2009). Unlabeled data improves word prediction. In 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009 (pp. 956-962). [5459347] (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2009.5459347