[visionlist] Special Issue Proposal for Multimedia Tools and Applications

Allan Ding allanzmding at gmail.com
Mon Nov 26 22:37:28 -05 2018

Please help announce our call paper for the special issue.


Multimedia applications naturally involve heterogeneous domain data, e.g.,
text, image, audio, and video. Data from heterogeneous domains tend to have
different marginal and conditional distributions. However, conventional
machine learning approaches assume that the training data and the test data
are from the same data distribution. Thus, there is an unavoidable obstacle
in the multimedia applications–how to mitigate the domain shifts in
cross-modal algorithms? Unfortunately, a majority of existing approaches in
the multimedia community ignored the problem or just left it for the future
research. Recently, transfer learning has been proven to be effective to
handle the domain shift problem and transfer knowledge from one domain to
the other related domains. Now it is the time to face the problem in
multimedia and investigate it with transfer learning!

This special issue is devoted to the publication of high-quality research
papers on transfer learning for various multimedia applications, such as
multimedia retrieval, classification, recommendation, multi-modal data
mining, etc. The special issue will seek for the original contribution of
works, which address the key challenges and problems.

Guest Editors

   - Dr. Jingjing Li, University of Electronic Science and Technology of
   China, China, Email: jjl at uestc.edu.cn
   - Dr. Zhengming Ding, Indiana University-Purdue University Indianapolis
   (IUPUI), USA, Email: zd2 at iu.edu
   - Dr. Weiqing Wang, School of Information Technology, Monash University,
   Australia, Email: Teresa.Wang at monash.edu


   - Transfer learning for multimedia retrieval/indexing
   - Transfer learning for image/video/music/audio retrieval.
   - Recommendation and Security
   - Transfer learning methods for the multimedia recommendation.
   - Transfer learning for multimedia security.
   - The survey, New ideas, and Multimedia tools.
   - Survey papers with regards to transfer learning for multimedia
   - New multimedia datasets for transfer learning.
   - New transfer learning tools for multimedia analysis.
   - Domain Adaptation, Multi-view learning, Zero-shot Learning
   - Deep / traditional, homogeneous / heterogeneous domain adaptation for
   - Multi-view learning algorithms for multimedia.
   - Zero-shot learning algorithms for multimedia.


Best Regards,
Dr. and Assistant Professor
Department of Computer, Information and Technology
Indiana University-Purdue University Indianapolis
799 West Michigan St., ET 301N,
Indianapolis IN 46202
Phone: +1-(317)274-9705
E-mail: z <allanzmding at gmail.com>d2 at iu.edu
*http://allanding.net/ <http://allanding.net/>*
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