[Face-research-list] 2nd CFP: The OMG-Empathy Prediction Challenge

Pablo Barros barros at informatik.uni-hamburg.de
Wed Nov 14 10:43:14 GMT 2018


The One-Minute Gradual-Empathy Prediction (OMG-Empathy) Competition
held in partnership with the IEEE International Conference on Automatic
Face and Gesture Recognition 2019 in Lille, France.


I. Aim and Scope

The ability to perceive, understand and respond to social interactions in a
human-like manner is one of the most desired capabilities in artificial
agents, particularly social robots. These skills are highly complex and
require a focus on several different aspects of research, including
affective understanding. An agent which is able to recognize, understand
and, most importantly, adapt to different human affective behaviors can
increase its own social capabilities by being able to interact and
communicate in a natural way.

Emotional expression perception and categorization are extremely popular in
the affective computing community. However, the inclusion of emotions in
the decision-making process of an agent is not considered in most of the
research in this field. To treat emotion expressions as the final goal,
although necessary, reduces the usability of such solutions in more complex
scenarios. To create a general affective model to be used as a modulator
for learning different cognitive tasks, such as modeling intrinsic
motivation, creativity, dialog processing, grounded learning, and
human-level communication, only emotion perception cannot be the pivotal
focus. The integration of perception with intrinsic concepts of emotional
understanding, such as a dynamic and evolving mood and affective memory, is
required to model the necessary complexity of an interaction and realize
adaptability in an agent's social behavior.

Such models are most necessary for the development of real-world social
systems, which would communicate and interact with humans in a natural way
on a day-to-day basis. This could become the next goal for research on
Human-Robot Interaction (HRI) and could be an essential part of the next
generation of social robots.

For this challenge, we designed, collected and annotated a novel corpus
based on human-human interaction. This novel corpus builds on top of the
experience we gathered while organizing the OMG-Emotion Recognition
Challenge, making use of state-of-the-art frameworks for data collection
and annotation.

The One-Minute Gradual Empathy datasets (OMG-Empathy) contain multi-modal
recordings of different individuals discussing predefined topics. One of
them, the actor, shares a story about themselves while the other, the
listener, reacts to it emotionally. We annotated each interaction based on
the listener's own assessment of how they felt while the interaction was
taking place.

We encourage the participants to propose state-of-the-art solutions not
only based on deep, recurrent and self-organizing neural networks but also
traditional methods for feature representation and data processing. We also
enforce that the use of contextual information, as well as personalized
solutions for empathy assessment, will be extremely important for the
development of competitive solutions.

II. Competition 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
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.

The training and validation samples will be given to the participants at
the beginning of the challenge together with all the associated labels. The
test set will be given to the participants without the associated labels.
The team`s predictions on the test set will be used to calculate the final
metrics of the challenge.

III. How to Participate

To participate in the challenge, please send us an email to barros @
informatik.uni-hamburg.de with the title "OMG-Empathy Team Registration".
This e-mail must contain the following information:
Team Name
Team Members
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 participating 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.

After the training period is over, the testing set will be released without
the associated annotations.

Each team must submit, via e-mail, their final predictions as a .csv file
for each video on the test set. Together with the final submission, each
team must send a short 2-4 pages paper describing their solution published
on Arxiv and the link for a GitHub page to their solution. If a team fails
to submit any of these items, their submission will be invalidated. Each
team can submit 3 complete submissions for each track.

IV. 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
5th of December 2018 - Final submission (Results and code)
7th of December 2018 - Final submission (Paper)
10th of December 2018 - Announcement of the winners

V. 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

Dr. 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.dehttp://www.pablobarros.nethttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.htmlhttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/
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