Call for contributions
*****IUI Dyadic IMPRESSION Recognition Challenge (virtual event)*****
https://simsimpression.unige.ch/ <https://simsimpression.unige.ch/>
IMPORTANT DATES
*
26/10/2021: Start of the Challenge, release of training data
*
07/01/2022: Abstract submission (validation results) and release of
the test data
*
24/01/2022: Final paper submission - End of the competition (test
results)
*
09/02/2022: Notification of paper acceptance
*
22/03/2022: Workshop held (online)
MOTIVATION
The Dyadic IMPRESSION Recognition Challenge, to be held in March 2022 in
conjunction with IUI 2022 in Helsinki, Finland, will be devoted to all
aspects of artificial intelligence and behavorial science for the
analysis of human-human interaction from multimodal data.
To advance and motivate the research on human bodily responses in dyadic
interactions, we organize the challenge which uses the open and
accessible multimodal IMPRESSION dataset. It addresses multimodal
recognition as well as dynamic multi-user recognition, where both
interlocutors’ information can be exploited.
THE CHALLENGE
The challenge aims at automatic impression recognition. This challenge
will focus on automatic impression recognition of multiple individuals
(i.e., the Receiver, that is, the person who forms an impression of the
other, i.e., the Emitter) during a dyadic interaction. Self-reported
impressions in the warmth and competence dimensions are given,
associated with synchronized face videos, eye movements and
physiological signals (including ECG, BVP and GSR) of both Emitters and
Receivers.
The challenge is composed of two phases:
*
Development phase: public training data will be released and
participants will develop their approaches and validate their
predictions using a validation set;
*
Test (final) phase: participants will need to submit their predicted
targets with respect to the test data, which will be released just a
few days before the end of the challenge. We will then rank
submissions by performance and communicate the results during the
workshop.
The evaluation consists of computing the average concordance correlation
coefficient (CCC) among the participants tested for the warmth and
competence between the predicted continuous values and the continuous
ground truth values.
All participants are invited to submit papers describing their solution
to the challenge and following the workshop submission guidelines of the
IUI conference.
Examples of potential submissions include (but are not limited to):
*
the combination of multimodal measures from either the Receiver or
Emitter;
*
the computation of synchrony features between the Receiver and Emitter;
*
deep learning architectures which combine features from the Receiver
and Emitter;
*
transfert learning approaches to extract features;
*
comparative studies of several approaches.
THE DATASET
The IMPRESSION dataset aims to focus on the development of automatic
approaches to study and understand the mechanisms of perception and
adaptation to verbal and nonverbal social signals in dyadic
interactions, taking into account individual and dyad characteristics.
To the best of our knowledge, there is no similar publicly available,
nonacted face-to-face dyadic dataset in the research field in terms of
participants, recorded sessions, and continuous impression labels in
warmth and competence. Detailed information about the IMPRESSION dataset
is provided on the challenge website and in the following paper:
https://archive-ouverte.unige.ch/unige:155675
<https://archive-ouverte.unige.ch/unige:155675>
Challenge Contact email:
[log in to unmask]
<mailto:[log in to unmask]>
Organizers:
*
Chen Wang, University of Geneva, Switzerland
*
Guillaume Chanel, University of Geneva, Switzerland
*
Beatrice Biancardi, LTCI, Télécom Paris, France
*
Chloé Clavel, LTCI, Télécom Paris, France
############################
Unsubscribe:
[log in to unmask]
If you don't already have a password for the LISTSERV.ACM.ORG server, we recommend
that you create one now. A LISTSERV password is linked to your email
address and can be used to access the web interface and all the lists to
which you are subscribed on the LISTSERV.ACM.ORG server.
To create a password, visit:
https://LISTSERV.ACM.ORG/SCRIPTS/WA-ACMLPX.CGI?GETPW1
Once you have created a password, you can log in and view or change your
subscription settings at:
https://LISTSERV.ACM.ORG/SCRIPTS/WA-ACMLPX.CGI?SUBED1=MM-INTEREST
|