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Workshop
Beyond classification: Machine Learning for next generation Computer Vision challenges
Craig Saunders · Jakob Verbeek · Svetlana Lazebnik

Fri Dec 10 07:30 AM -- 06:30 PM (PST) @ Westin: Alpine BC
Event URL: http://sites.google.com/site/mlngcvc/ »

This workshop seeks to excite and inform researchers to tackle the next level of problems in the area of Computer Vision. The idea is to both give Computer Vision researchers access to the latest Machine Learning research, and also to communicate to researchers in the machine learning community some of the latest challenges in computer vision, in order to stimulate the emergence of the next generation of learning techniques. The workshop itself is motivated from several different points of view:

\begin{enumerate}
\item There is a great interest in and take-up of machine learning techniques in the computer vision community. In top vision conferences such as CVPR, machine learning is prevalent: there is widespread use of Bayesian Techniques, Kernel Methods, Structured Prediction, Deep Learning, etc.; and many vision conferences have featured invited speakers from the machine learning community.

\item Despite the quality of this research and the significant adoption of machine learning techniques, often such techniques are used as black box'' parts of a pipeline, performing traditional tasks such as classification or feature selection, rather than fundamentally taking a learning approach to solving some of the unique problems arising in real-world vision applications. <br> <br>\item Beyond object recognition and robot navigation, many interesting problems in computer vision are less well known. These include more <br>complex tasks such as joint geometric/semantic scene parsing, object discovery, modeling of visual attributes, image aesthetics, etc. <br> <br>\item Even within the domain ofclassic'' recognition systems, we also face significant challenges in scaling up machine learning techniques to millions of images and
thousands of categories (consider for example the
ImageNet data set).

\item Images often come with extra multi-modal information (social network graphs, user preference, implicit feedback indicators, etc) and this information is often poorly used, or integrated in an ad-hoc fashion.

\end{enumerate}

This workshop therefore seeks to bring together machine learning and computer vision researchers to discuss these challenges, show current progress, highlight open questions and stimulate promising future research.

Author Information

Craig Saunders (Xerox Research Centre Europe)
Jakob Verbeek (INRIA)
Svetlana Lazebnik (UIUC)

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