In this paper we present a new scheme for detection and tracking of specific objects in a knowledge-based framework. The scheme uses a supervised learning method: Support Vector M...
Lionel Carminati, Jenny Benois-Pineau, Christian J...
Can a machine learn to perceive emotions as evoked by an artwork? Here we propose an emotion categorization system, trained by ground truth from psychology studies. The training d...
Victoria Yanulevskaya, Jan van Gemert, Katharina R...
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is qui...
Alexander C. Berg, Hao Zhang 0003, Jitendra Malik,...
In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of n...
Cross-domain learning methods have shown promising
results by leveraging labeled patterns from auxiliary domains
to learn a robust classifier for target domain, which
has a limi...
Dong Xu, Ivor Wai-Hung Tsang, Lixin Duan, Stephen ...
Support Vector Machines (SVM) are one of the most useful
techniques in classification problems. One clear example
is face recognition. However, SVM cannot be applied
when the fe...
I am a research scientist at the Department Empirical Inference (Head. Prof. Dr. Bernhard Schölkopf) of the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. ...