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活动简介

Semantic Multimedia Computing aims to develop algorithms for enriching, accessing, and searching large quantities of data. Such algorithms lie at the core of tomorrows’ search engines and large-scale recommender systems. Thus, Semantic Multimedia Computing sets its focus on developing systems that are oriented to the needs of users, and that solve the challenges faced by large-scale online content and service providers. Multimedia data analytics has also applications in the full range of fields that benefit from data science, including health, telecom, entertainment, geosciences, etc.

As a result, Semantic Multimedia Computing deals with the development of technologies that make possible optimized interaction with large collections of multimedia data (e.g., images, video, and music) in real-world contexts (e.g., within social networks). That also requires a combination of mathematical models, machine learning techniques, and practical skills in algorithm development and evaluation.

This workshop aims at providing researchers and practitioners from different areas (multimedia information retrieval, recommender systems, multimedia signal processing, social network analysis, human computation) with an interdisciplinary forum to present, discuss, and exchange ideas that address the challenges of next-generation systems dealing with Semantic Multimedia Computing. The workshop seeks submissions from academia, government, and industry presenting novel research results in all practical and theoretical aspects of Semantic Multimedia Computing.

征稿信息

重要日期

2016-12-08
初稿截稿日期
2016-12-15
初稿录用日期
2016-12-30
终稿截稿日期

征稿范围

Topics of interest include, but are not limited to:

Multimedia content analysis and search

  • Semantics extraction from multimedia data.

  • Multi-modal query expansion.

  • Multi-source search result reranking.

Multimedia information retrieval in a social network context

  • Modeling information propagation and relationships in social networks.

  • Collaborative recommender systems.

  • Social recommendation.

Interaction with multimedia content

  • (Affective) User profiling.

  • User (search/uploader) intent.

  • Query failure prediction.

  • Quality of multimedia experience.

Multimedia content management

  • Multimedia databases and dataspaces.

  • Entity retrieval.

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重要日期
  • 会议日期

    01月30日

    2017

    02月01日

    2017

  • 12月08日 2016

    初稿截稿日期

  • 12月15日 2016

    初稿录用通知日期

  • 12月30日 2016

    终稿截稿日期

  • 02月01日 2017

    注册截止日期

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