Connectionists: CFP: The OMG-Emotion Recognition Challenge

[Pablo Barros]

The OMG-Emotion Recognition Challenge


CALL FOR PARTICIPATION

The One-Minute Gradual-Emotion Recognition (OMG-Emotion)
held in partnership with the WCCI/IJCNN 2018 in Rio de Janeiro, Brazil.

https://www2.informatik.uni-hamburg.de/wtm/OMG-EmotionChallenge/


 I. Aim and Scope

Our One-Minute-Gradual Emotion Dataset (OMG-Emotion Dataset) is composed
of 420 relatively long emotion videos with an average length of 1
minute, collected from a variety of Youtube channels. The videos were
selected automatically based on specific search terms related to the
term ``monologue''. Using monologue videos allowed for different
emotional behaviors to be presented in one context and that changes
gradually over time. Videos were separated into clips based on
utterances, and each utterance was annotated by at least five
independent subjects using the Amazon Mechanical Turk tool. To maintain
the contextual information for each video, each annotator watched the
clips of a video in sequence and had to annotate each video using an
arousal/valence scale and a categorical emotion based on the universal
emotions from Ekman.


We release the dataset with the gold standar for arousal and valence as
well the invidivual annotations for each reviewer, which can help the
development of different models. We will calculate the final Congruence
Correlation Coefficient  against the gold standard for each video. We
also distribute the transcripts of what was spoken in each of the
videos, as the contextual information is important to determine gradual
emotional change through the utterances. The participants are encouraged
to use crossmodal information in their models, as the videos were
labeled by humans without distinction of any modality. We also will let
available to the participant teams a set of scripts which will help them
to pre-process the dataset and evaluate their model during in the
training phase.

We encourage the use of neural-computation models based on deep
learning, sel-organization and recurrent neural networks, just to
mention some of them, as they present the sate-of-the-art performance in
such tasks.



II. How to Participate

To participate, please send us an email to
barros at informatik.uni-hamburg.de with the title "OMG-Emotion Recognition
Team Registration". This e-mail must contain the following information:
Team Name
Team Members
Affiliation

Each team can have a maximum of 5 participants. You will receive from us
the access to the dataset and all the important information about how to
train and evaluate your models.
For the final submission, each team will have to send us a .csv file
containing the final arousal/valence values for each of the utterances
on the test dataset. We also request a link to a GitHub repository where
your solution must be stored, and a link to an ArXiv paper with 4-6
pages describing your model and results. The best papers will be invited
to submit their detailed research to a journal yet to be specified.
Also, the best participating teams will hold an oral presentation about
their solution during the WCCI/IJCNN 2018 conference.


III. Important Dates

Publishing of training and validation data with annotations: March 14,
2018.
Publishing of the test data, and an opening of the online submission:
April 11, 2018.
Closing of the submission portal: April 13, 2018.
Announcement of the winner through the submission portal: April 18, 2018.



IV. Organization

Pablo Barros, University of Hamburg, Germany
Egor Lakomkin, University of Hamburg, Germany
Henrique Siqueira, Hamburg University, Germany
Alexander Sutherland, Hamburg University, Germany
Stefan Wermter, Hamburg University, Germany--

Dr.rer.nat. Pablo Barros
Postdoctoral Research Associate - Crossmodal Learning Project (CML)
Knowledge Technology
Department of Informatics
University of Hamburg
Vogt-Koelln-Str. 30
22527 Hamburg, Germany
Phone: +49 40 42883 2535
Fax: +49 40 42883 2515
barros at informatik.uni-hamburg.de
https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.html
https://www.inf.uni-hamburg.de/en/inst/ab/wtm/