(Apologies for possible cross-posting)
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CALL FOR PAPERS
MUWS 2023 - The 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media
co-located with 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023).

October 22 2023, Birmingham, UK
More Info: https://muws-workshop.github.io/
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Aim and Scope

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like Computer Vision, Natural Language Processing (NLP), Speech Processing, Human-Computer Interaction, and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media.

The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop will be an interactive event and include keynotes by relevant experts, poster and demo sessions, research presentations and discussion.

Particular areas of interest include, but are not limited to:

    - Modeling human impressions in the context of the Web and Social Media
    - Cross-modal and semantic relations
    - Incorporating multi-disciplinary theories such as Semiotics or Gestalt-Theory into multimodal analyses
    - Measuring and analyzing biases such as cultural bias, social bias, multilingual bias, and related topics in the context of the Web and Social Media
    - Multimodal human perception understanding
    - Multimodal sentiment/emotion/sarcasm recognition
    - Multimodal hate speech detection
    - Multimodal misinformation detection
    - Multimodal content understanding and analysis
    - Multimodal rhetoric in online media


Submission Instructions

We welcome contributions with 8-15 pages that address the topics of interest. Papers should follow the Springer LNCS proceedings style. All submissions must be written in English and must be formatted according to the proceedings style. Accepted papers will be given the option to be published as part of a CEUR Workshop Proceedings.

Submission Page: https://easychair.org/conferences/?conf=muws2023


Important Dates

    Submission deadline: August 18th, 2023
    Paper notification: September 15th, 2023
    Workshop date: October 22nd, 2023

Organizing Committee

Gullal S. Cheema, L3S Research Center, Leibniz University Hannover, Germany
Sherzod Hakimov, University of Potsdam, Potsdam, Germany
Marc A. Kastner, Kyoto University, Kyoto, Japan
Noa Garcia, Osaka University, Osaka, Japan

Contact

All questions about the workshop should be emailed to: muws2023 (at sign) easychair.org


--
Dr. Marc A. Kastner
Assistant Professor
Kyoto University, Graduate School of Informatics
Intelligent Science and Technology Course, Computer Vision Lab
https://www.marc-kastner.com/

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