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                         *CALL FOR PAPERS*

                  1st International Workshop on:
            *Robust Multimedia Learning in Broad Domains*
            http://staff.science.uva.nl/~cgmsnoek/robust/

              In conjunction with ACM Multimedia 2008
                 October 31, Vancouver, BC, Canada
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Aims and scope
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The focus of the workshop is on the unique opportunities, challenges,
and solutions for robust multimedia learning across different
domains. Since the revolution in web information retrieval, multimedia
analysis and retrieval has been regarded as the next grand
challenge. Media-sharing sites like Flickr, YouTube, and Last.fm have
brought huge amounts of multimedia resources, reflecting all aspects
of social life, with open access to anyone. Such explosions of
multimedia data and associated tags provided by amateur-consumers,
bring new opportunities for researchers to deepen our already acquired
understanding. So far, our knowledge has been restricted to specific
domains including sport video, news video, and natural images. The
proposed workshop strives to broaden our understanding in this
emerging area, with an ultimate aim to make unstructured multimedia
data from broad domains accessible, reusable, searchable, and
manageable.

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Multimedia learning
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As a corner stone in this field, machine learning techniques are
 densely employed and models are built for tasks varying from
 categorizing images into scenes, detecting semantic concepts in
 video, and recommending music by their content similarity, to name a
 few. Given the variety of domains where multimedia is generated these
 days, there is a clear demand to generalize and adapt models trained
 from one domain to other domains, both for scientific, technological,
 economic, and performance considerations. For example, the
 amateur-tagged images with partially, and ambiguous labels may serve
 as a useful resource for novel video analysis and retrieval
 applications. Another scenario might be to utilize semantic concept
 models trained for news video, for detection in video
 surveillance. Current research has shown impressive results in
 multimedia analysis for semantic concepts, but evaluations have
 repeatedly shown that these good results do not transfer well to
 similar concepts in other domains. Thus, robust learning approaches
 are in urgent demand given the potential impact they may produce and
 the abundance of existing but unexplored labeled multimedia resources
 available.

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Topics of interest
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We solicit submissions that introduce key concepts, discuss
theoretical frameworks and technical approaches, challenges, research
opportunities, and open issues in areas of interest in robust
multimedia learning, including (but not limited to) the following
areas:

    * structured domain modeling algorithms;
    * robust content and context dependent learning;
    * online/social multimedia analysis and mining;
    * transfer learning with human-in-the loop;
    * transfer learning models in multimedia analysis/retrieval;
    * evaluation and benchmark metrics for broad domain multimedia learning;
    * success and failure analysis for robust multimedia learning;
    * multimedia learning systems and applications in and cross domains of biometrics, mobile media, personal media, and the arts, among others;
    * future challenges for robust multimedia learning.

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Paper submission
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All authors are requested to use the workshop EDAS system
(http://edas.info/newPaper.php?c=6649&track=4665&) for sending in
their submissions. The pdf submissions should be up to 8 pages in the
ACM style in English. All submissions will be peer-reviewed by at
least 3 members of the program committee.

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Important dates
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    * July 5: Submission of full paper
    * July 26: Notification of acceptance
    * August 1: Camera-ready submission
    * October 31: Workshop

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Organizing Committee
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    * Alexander G. Hauptmann, Carnegie Mellon University, USA
    * Cees G.M. Snoek, University of Amsterdam, The Netherlands
    * Jianmin Li, Tsinghua University, P.R. China

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Program Committee
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    * Shih-Fu Chang, Columbia University, USA
    * Tat-Seng Chua, National University of Singapore, Singapore
    * Alberto Del Bimbo, Universitŕ degli Studi di Firenze, Italy
    * Alan Hanjalic, TU Delft, The Netherlands
    * Winston Hsu, National Taiwan University, Taiwan
    * Xian-Sheng Hua, Microsoft Research Asia, P.R. China
    * Michael Lew, Leiden University, The Netherlands
    * Rainer Lienhart, Augsburg University, Germany
    * Jiebo Luo, Kodak Research Laboratories, USA
    * Stéhane Marchand-Maillet, University of Geneva, Switzerland
    * Apostol (Paul) Natsev, IBM Research, USA
    * Chong-Wah Ngo, City University of Hong Kong, P.R. China
    * Shin'ichi Satoh, National Institute of Informatics, Japan
    * Qi Tian, University of Texas at San Antonio, USA
    * Cor Veenman, University of Amsterdam, The Netherlands
    * Dong Wang, Tsinghua University, P.R. China 
    * Marcel Worring, University of Amsterdam, The Netherlands
    * Rong Yan, IBM Research, USA
    * Jun Yang, Carnegie Mellon University, USA
    * Yimin Zhang, Intel China Research Center, P.R. China
    * Zhi-Hua Zhou, Nanjing University, P.R. China