Paper
Paper submission (all participants): Please be reminded that a paper submission and at least one upload on the test set are mandatory for participation in the Challenge. However, paper contributions within the scope (below) are also welcome if the authors do not intend to participate in the Challenge itself. In any case, please submit your paper until 08 August 2021 using the standard style info and respecting length limits, and submit to the regular submission system.
Manuscripts should follow the ACM MM 2021 paper format. Authors should submit papers as PDF file.
Submission will be via CMT:
Track: 2nd Multimodal Sentiment Analysis Challenge (MuSe 2021)
Important: The papers will undergo the normal review process (double-blind).
The introductory Paper on the Challenge provides extensive descriptions and baseline results. All participants will be asked to avoid repetitions of Challenge, data, or feature descriptions in their submissions – of course, they have to describe shortly the essentials of the databases dealt with – but include the following citation:
In BibTeX, the reference is:
@inproceedings{stappen2021muse,
title={The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress},
author={Stappen, Lukas and Baird, Alice and Christ, Lukas and Schumann, Lea and Sertolli, Benjamin and Messner, Eva-Maria and Cambria, Erik and Zhao, Guoying and Schuller, Bj{\"o}rn W},
year = {2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 2nd International on Multimodal Sentiment Analysis Challenge and Workshop},
location = {Chengdu, China}
}
Please acknowledge the dataset paper for the MuSe data set:
@article{stappen2021multimodal,
title={The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements},
author={Stappen, Lukas and Baird, Alice and Schumann, Lea and Schuller, Bj{\"o}rn},
journal={IEEE Transactions on Affective Computing},
volume={-},
number={-},
pages={-},
year={2021},
publisher={IEEE}
}
Please note:
The submission deadline is at 11:59 p.m. of the stated deadline date Anywhere on Earth.
Papers accepted for the workshop will be allocated 5-8 pages (plus additional pages for the references) in the proceedings and have to be presented.
Participants may contribute to all Sub-Challenges at a time. A training and development partitioning will allow for tests and results to be reported by the participants apart from their results on the official test set. Papers may well report additional results on other databases. An additional publication is planned that summarises all results of the Challenge.
Papers can but do not have to repeat the descriptions of database, labels, partitioning etc. of the Sub-Challenge corpora but cite the introductive paper (cf. above). Each paper will receive at least three reviews. Acceptance will be based on relevance to the workshop, novelty, and technical quality.
Other 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