We describe an unsupervised learning algorithm for extracting sparse and locally shift-invariant features. We also devise a principled procedure for learning hierarchies of invari...
In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-...
Cast shadows induced by moving objects often cause serious problems to many vision applications. We present in this paper an online statistical learning approach to model the backg...
Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have n...
Rodrigo Sigala, Thomas Serre, Tomaso Poggio, Marti...
We present a biologically motivated architecture for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The system...