MuSe Submission

Submission Policy

In submitting a manuscript to this workshop, the authors acknowledge that no paper substantially similar in content has been submitted to another conference or workshop.

Manuscripts should follow the ACM MM 2020 paper format. Authors should submit papers as a PDF file. Submission will be via CMT:

• https://cmt3.research.microsoft.com/MMW2020

• Track: 1st International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop

Papers accepted for the workshop will be allocated 6-8 pages (plus additional pages for the references) in the proceedings of ACM MM 2020.

MuSe 2020 reviewing is double blind. Reviewing will be by members of the program committee. Each paper will receive at least three reviews. Acceptance will be based on relevance to the workshop, novelty, and technical quality.

Please do not forget to cite the baseline paper, if your submission makes use of this corpus:

Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Björn W Schuller, Iulia Lefter, Erik Cambria, and Ioannis Kompatsiaris. 2020. MuSe 2020 Challenge and Workshop: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media. In 1st International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop, co-located with the 28th ACM International Conference on Multimedia (ACM MM). ACM.

@inproceedings{stappen2020muse,
title={MuSe 2020 Challenge and Workshop: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media},
author={Stappen, Lukas and Baird, Alice and Rizos, Georgios and Tzirakis, Panagiotis and Du, Xinchen and Hafner, Felix and Schumann, Lea and Mallol-Ragolta, Adria and Schuller, Bj{\"o}rn W and Lefter, Iulia and Cambria, Erik and Kompatsiaris, Ioannis},
booktitle={1st International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop, co-located with the 28th ACM International Conference on Multimedia (ACM MM)},
year={2020},
organization={ACM}}

Contributions

A core discipline of multimedia research is to handle big data and develop effective embedding and fusing strategies in order to combine multiple modalities and understand multimedia content better. The recordings of the database are from the fast growing source of big data - user-generated content in a natural setting. They also inherently contain three modalities: video in the form of domain-context, perceived gestures and facial expressions; audio through voice prosody and intonations as well as domain-dependent environment sounds; and text via natural spoken language.

We encouraged contributions aiming at highest performance w.r.t. the baselines provided by the organisers, and contributions aiming at finding new and interesting insights w.r.t. to the topic of these challenges, especially:

Multimodal Affect/ Emotion/ Sentiment Sensing

• • • • Audio-based Emotion Recognition

      • Video-based Emotion Recognition

      • Text-based Sentiment/ Emotion Recognition

      • Multimodal Representation Learning

      • Transfer Learning

      • Semi-supervised and Unsupervised Learning

      • Multi-view learning of Multiple Dimensions

      • Aspect Extraction

      • Context in Emotion Recognition

Application

• • • • Multimedia Coding and Retrieval

      • Mobile and Online Applications