We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
In this paper, word sense dismnbiguation (WSD) accuracy achievable by a probabilistic classifier, using very milfimal training sets, is investigated. \Ve made the assuml)tiou that...
A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. The performance of these methods improves when relations among th...
Valero Laparra, Juan Gutierrez, Gustavo Camps-Vall...