TY - GEN
T1 - Unlabeled data improves word prediction
AU - Loeff, Nicolas
AU - Farhadi, Ali
AU - Endres, Ian
AU - Forsyth, David A.
PY - 2009
Y1 - 2009
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=77953189631&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953189631&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2009.5459347
DO - 10.1109/ICCV.2009.5459347
M3 - Conference contribution
AN - SCOPUS:77953189631
SN - 9781424444205
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 956
EP - 962
BT - 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
T2 - 12th International Conference on Computer Vision, ICCV 2009
Y2 - 29 September 2009 through 2 October 2009
ER -