Yesterday I attended to this workshop at EPFL:
It was a good opportunity to see old friends and colleagues, and listen about their latest research. In general, the quality of the talks was quite good, ranging from very theoretical machine learning (sparse coding, optimization, etc.) to commercial applications of computer vision (www.faceshift.com).
Somewhere in the middle of that spectrum, I also quite liked the talk about learning image local descriptors (BRIEF and LBGM) as a compact and efficient alternative to SIFT or SURF, which are hand-designed, slower and use more bits. There were also applications to speech, face analysis and even remote sensing.
Have a look at the program and keep an eye on it in the coming days, as the slides will probably become available. You will find several other interesting talks:
Lately, I have been working with Deformable Models and I am surprised by how well they can work.
In the video above I am using an Inverse Compositional Active Appearance Model, which was trained with images of myself. It’s specially tuned for my face, but I still find it quite impressive how well it can track my face in realtime!
On the other hand, this model is quite sensitive to lighting conditions and partial occlusions. Training it, is also somehow of an art, because, as opposed to discriminative models, increasing the amount of training data might actually decrease performance. This happens because we use PCA to learn the linear models of shape and texture, which will degrade if data has too much variation or noise.
Still, it’s quite impressive what one can achieve by annotating a few images (about 50, in this case). In addition, as one annotates images, one can start training models that will help us landmark the next ones (in a process of “bootstrapping”, similar to the one in compilers).