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 semisupervised
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).