ICCV 2015, the International Conference on Computer Vision, is one of the premier venues for computer vision research, together with the CVPR conference. This ICCV is happening in Santiago, Chile, a beautiful city with amazing food.
The computer vision community is growing, and this ICCV is the largest so far (1460 attendees, 525 papers). Since a few years computer vision is broadly relevant for the industry and there are no less than 22 companies sponsoring the conference. The acceptance rate this year was 30.92%, with the acceptance for oral presentations at 3.30%. All papers of the conference are available as open-access PDF here.
There was a lot of interesting work presented on the first day, but here is my subjective selection of interesting work.
Aligning Books and Movies
By Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler.
Movies and the books they are based on form a rich paired data source. In this work the authors propose a recurrent neural network model to align these two sources semantically. The challenge is that movies and books are often substantially different, but apparently modern recurrent neural networks have enough semantic discrimination ability to enable such alignment.
Convolutional Color Constancy
By Jonathan Barron.
Color constancy deals with the correction of colors in digital images. While there have been a large number of works in this area, the issue remains challenging and important.
In this work the author convincingly demonstrates that common changes in colors correspond to simple translation of a color histogram in a transformed 2D histogram space. Then, the problem of correcting for these translations can be posed as simply recognizing the true center position of the observed color histogram and undoing the translation.
Self-Calibration of Optical Lenses
By Michael Hirsch and Bernhard Schoelkopf.
Both cheap and expensive camera lenses suffer from many optical effects, leading to deterioration in image quality. This work proposes an automatic way to obtain non-parametric kernel estimates of the point spread functions characterising a lens. The resulting model can then be used to deblur images. In effect, this allows better image quality even when using cheap lenses.
The second day is available now.