Website: https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html

Organization

Pablo Barros, University of Hamburg, Germany
Nikhil Churamani, University of Cambridge, United Kingdom
Angelica Lim, Simon Fraser University, Canada
Stefan Wermter, Hamburg University, Germany

OMG-Empathy Dataset More information can be found at the OMG Empathy Dataset repository

Tracks

We let available for the challenge a pre-defined set of training, validation and testing samples. We separate our samples based on each story: 4 stories for training, 1 for validation and 3 for testing. Each story sample is composed of 10 videos with interactions, one for each listener. Although using the same training, validation and testing data split, we propose two tracks which will measure different aspects of the self-assessed empathy:

The Personalized Empathy track, where each team must predict the empathy of a specific person. We will evaluate the ability of proposed models to learn the empathic behavior of each of the subjects over a newly perceived story. We encourage the teams to develop models which take into consideration the individual behavior of each subject in the training data.

The Generalized Empathy track, where the teams must predict the general behavior of all the participants over each story. We will measure the performance of the proposed models to learn a general empathic measure for each of the stories individually. We encourage the proposed models to take into consideration the aggregated behavior of all the participants for each story, and to generalize this behavior in a newly perceived story.

Baseline

As a baseline for both protocols, we decided to use a state-of-the-art model for emotion recognition. By providing a baseline with an emotion recognition model, we hope to encourage others to use more specific solutions for empathy tracking.

We choose to calculate the valence of each of our videos using a model trained on the recent OMG-Emotion Recognition challenge[1]. This model makes use of a hybrid neural network for multimodal emotion recognition. It uses a temporal self-organizing layer to learn prototypes of expressions which are used for emotion recognition classification.

To provide a proper baseline for the dataset, and to encourage the development of computational models which are not based on perception only, we calculated the predicted valence using the perception information from the actor (only the actor facial expression and audio) and from the listener (only the listener face expression and audio) individually. We then calculate the CCC between the perception model and the self-assessment annotations using both protocols. This results were calculated using the validation set.

[1] Barros, P., Barakova, E., & Wermter, S. (2018). A Deep Neural Model Of Emotion Appraisal. arXiv preprint arXiv:1808.00252.

Listener Only

Personalized Track

Generalized Track

Actor Only

Personalized Track

Generalized Track

Dataset Structure

The dataset is separated into .mp4 videos. Each video name contain the story and subject information. The annotations follow the same name structure. Each .csv contain a header ("valence") and each row of the file represents the valence of one frame of the video.

Scripts

We provide the following scripts:

• OMG_empathy_extract_audio.py: a script used to extract an audio file (in .wav format) for all the videos of the dataset.

• OMG_empathy_extract_faces.py: a script used to extract the faces (from both actors and subjects) for all the videos of the dataset.

• calculateCCC.py: a script used to calculate the CCC of the output of a model and the validation set for both tracks.

To calculate the CCC for both tracks, the script takes as arguments two folders: one with the validation .csv files and one with the model's .csv files. Be aware that the folders must contain the same amount of files and they must have he same file name - following the same structure as the annotations: Subject_X_Story_X.csv. Each file must contain a header ("valence") and each row of the file must be the valence output for each frame of the video.

OMG Empathy Challenge 2019

Important dates:

• 25th of September 2018 - Opening of the Challenge - Team registrations begin
• 1st of October 2018 - Training/validation data and annotation available
• 3rd of December 2018 - Test data release
• 7th of December 2018 - Final submission (Results and code)
• 13th of December 2018 - Final submission (Paper)
• 14th of December 2018 - Announcement of the winners

To participate to the challenge, please send us an email to [email protected] / [email protected] with the title "OMG-Empathy Team Registration". This e-mail must contain the following information:

• Team Name
• Team Members
• Affiliation
• Participating tracks

We split the corpus into three subsets: training, validation and testing. The participants will receive the training and validation sets, together with the associated annotations once they subscribe to the challenge. The subscription will be done via e-mail. Each participant team must consist of 1 to 5 participants and must agree to use the data only for scientific purposes. Each team can choose to take part in one or both the tracks.

final results: https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy2018_results2018.html

Access do the dataset

To have full access to this corpus, please send an e-mail with your name, affiliation and research summary to: pablovin @ gmail.com

License

This corpus is distributed under the Creative Commons CC BY-NC-SA 3.0 DE license. If you use this corpus, you have to agree with the following itens:

• To cite our reference in any of your papers that make any use of the database.
• The references are provided at the end of this page.
• To use the corpus for research purpose only.
• To not provide the corpus to any second parties.
• To delete the dataset as soon as you finish using it.

Reference paper

• Barros, P., Churamani, N., Lim, A., & Wermter, S. (2019, September). The omg-empathy dataset: Evaluating the impact of affective behavior in storytelling. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 1-7). IEEE. https://arxiv.org/abs/1908.11706

More information

• our website: https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html
• You can also find usefull scripts for processing the dataset here: https://github.com/knowledgetechnologyuhh/OMGEmpathyChallenge

For more information: [email protected]