@inproceedings{Guha2020ATQAM-51420,
  year={2020},
  doi={10.1145/3394171.3421895},
  title={ATQAM/MAST'20 : Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends},
  isbn={978-1-4503-7988-5},
  publisher={ACM},
  address={New York},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia (MM ’20)},
  pages={4758--4760},
  editor={Chen, Chang Wen},
  author={Guha, Tanaya and Hosu, Vlad and Saupe, Dietmar and Goldlücke, Bastian and Kumar, Naveen and Lin, Weisi and Martinez, Victor and Somandepalli, Krishna and Narayanan, Shrikanth and Cheng, Wen-Huang and McLaughlin, Kree}
}

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    <dcterms:title>ATQAM/MAST'20 : Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends</dcterms:title>
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    <dcterms:abstract xml:lang="eng">The Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends (ATQAM/ MAST) aims to bring together researchers and professionals working in fields ranging from computer vision, multimedia computing, multimodal signal processing to psychology and social sciences. It is divided into two tracks: ATQAM and MAST. ATQAM track: Visual quality assessment techniques can be divided into image and video technical quality assessment (IQA and VQA, or broadly TQA) and aesthetics quality assessment (AQA). While TQA is a long-standing field, having its roots in media compression, AQA is relatively young. Both have received increased attention with developments in deep learning. The topics have mostly been studied separately, even though they deal with similar aspects of the underlying subjective experience of media. The aim is to bring together individuals in the two fields of TQA and AQA for the sharing of ideas and discussions on current trends, developments, issues, and future directions. MAST track: The research area of media content analytics has been traditionally used to refer to applications involving inference of higher-level semantics from multimedia content. However, multimedia is typically created for human consumption, and we believe it is necessary to adopt a human-centered approach to this analysis, which would not only enable a better understanding of how viewers engage with content but also how they impact each other in the process.</dcterms:abstract>
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