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Special Section on Multimodal Understanding of Social, Affective, and Subjective Attributes

Published:24 January 2019Publication History
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Abstract

Multimedia scientists have largely focused their research on the recognition of tangible properties of data such as objects and scenes. Recently, the field has started evolving toward the modeling of more complex properties. For example, the understanding of social, affective, and subjective attributes of visual data has attracted the attention of many research teams at the crossroads of computer vision, multimedia, and social sciences. These intangible attributes include, for example, visual beauty, video popularity, or user behavior. Multiple, diverse challenges arise when modeling such properties from multimedia data. The sections concern technical aspects such as reliable groundtruth collection, the effective learning of subjective properties, or the impact of context in subjective perception; see Refs. [2] and [3].

References

  1. Xavier Alameda-Pineda, Andrea Pilzer, Dan Xu, Nicu Sebe, and Elisa Ricci. 2017. Viraliency: Pooling local viraliry. In IEEE CVPR.Google ScholarGoogle Scholar
  2. Xavier Alameda-Pineda, Miriam Redi, Nicu Sebe, Shih-Fu Chang, and Jiebo Luo. 2018. ACM MM’18 workshop on understanding subjective attributes of data, multimodal recognition of evoked emotions. In ACM International Conference on Multimedia.Google ScholarGoogle Scholar
  3. Xavier Alameda-Pineda, Miriam Redi, Mohammad Soleymani, Nicu Sebe, Shih-Fu Chang, and Samuel Gosling. 2017. MUSA2—First ACM workshop on multimodal understanding of social, affective and subjective attributes. In ACM Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Xavier Alameda-Pineda, Elisa Ricci, Yan Yan, and Nicu Sebe. 2016. Recognizing emotions from abstract paintings using non-linear matrix completion. In IEEE CVPR.Google ScholarGoogle Scholar
  5. Michael Gygli, Helmut Grabner, Hayko Riemenschneider, and Luc Van Gool. 2013. The interestingness of images. In ICCV. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Brendan Jou, Tao Chen, Nikolaos Pappas, Miriam Redi, Mercan Topkara, and Shih-Fu Chang. 2015. Visual affect around the world: A large-scale multilingual visual sentiment ontology. In ACM International Conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Aditya Khosla, Atish Das Sarma, and Raffay Hamid. 2014. What makes an image popular? In WWW. 867--876. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Lorenzo Porzi, Samuel Rota Bulò, Bruno Lepri, and Elisa Ricci. 2015. Predicting and understanding urban perception with convolutional neural networks. In ACM MM. 139--148. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, Elisa Ricci, and Nicu Sebe. 2017. How to make an image more memorable? A deep style transfer approach. In ACM ICMR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva. 2014. Learning deep features for scene recognition using places database. In NIPS. 487--495. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 15, Issue 1s
          Special Section on Deep Learning for Intelligent Multimedia Analytics and Special Section on Multi-Modal Understanding of Social, Affective and Subjective Attributes of Data
          January 2019
          265 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3309769
          Issue’s Table of Contents

          Copyright © 2019 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 24 January 2019
          Published in tomm Volume 15, Issue 1s

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