The OMG-Emotion Recognition Challenge



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

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 standard for arousal and valence as
well the individual 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 participating 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, self-organization, and recurrent neural networks, just to
mention some of them, as they present the state-of-the-art performance in
such tasks.

II. How to Participate

To participate, please send us an email to with the title “OMG-Emotion Recognition
Team Registration”. This e-mail must contain the following information:
Team Name
Team Members

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,
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

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Workshop on Intelligent Assistive Computing at IEEE WCCI – July 8th, 2018

Workshop on Intelligent Assistive Computing at IEEE WCCI – July 8th, 2018

First call for papers

We kindly invite you to submit your contributions to the workshop
to be held in Rio de Janeiro, Brazil.

Assistive technologies have the goal to provide greater quality of life and independence in domestic environments by enhancing or changing the way people perform activities of daily living (ADLs), tailoring specific functionalities to the needs of the users. Significant advances have been made in intelligent adaptive technologies that adopt state-of-the-art learning systems applied to assistive and health-care-related domains. Prominent examples are fall detection systems that can detect domestic fall events through the use of wearable physiological sensors or non-invasive vision-based approaches, and body gait assessment for physical rehabilitation and the detection of abnormal body motion patterns, e.g., linked to age-related cognitive declines. In addition to an adequate sensor technology, such approaches require methods able to process rich streams of (often noisy) information with real-time performance.

Assistive technology has been the focus of research in the past decades. However, it flourished in the past years with the fast development of personal robots, smart homes, and embedded systems. The focus of this workshop is to gather neural network researchers, both with application and development focus, working on or being interested in building and deploying such systems. Despite the high impact and application potential of assistive systems for the society, there is still a significant gap between what is developed by researchers and the applicability of such solutions in real-world scenarios. This workshop will discuss how to alleviate this gap with help of the latest neural network research such as deep, self-organizing, generative and recurrent neural models for adaptable lifelong learning applications. In this workshop, we aim at collecting novel methods, computational models, and experimental strategies for intelligent assistive systems such as body motion and behavior assessment, rehabilitation and assisted living technologies, multisensory frameworks, navigation assistance, affective computing, and more accessible human-computer interaction.

The primary list of topics covers the following topics (but not limited to):

– Machine learning and neural networks for assistive computing
– Behavioral studies on assistive computing
– Models of behavior processing and learning
– New theories and findings on assistive computing
– human-machine, human-agent, and human-robot interaction focused on assistive computing
– Brain-machine interfaces for assistive computing
– Crossmodal models for assistive computing

Invited speakers
– Igor Farkas, Comenius University
– Giulio Sandini, Istituto Italiano di Tecnologia (IIT)
– Stefan Wermter, University of Hamburg

Call for contributions
Participants are required to submit a contribution as:

– Extended abstract (maximum 2 pages)
– Short paper (maximum 4 pages)

Selected contributions will be presented during the workshop as
spotlight talks and in a poster session.

Important dates
April 6, 2018 – Paper submission deadline
May 4, 2018 – Notification of acceptance
May 25, 2018 – Camera-ready version
July 8, 2018 – Workshop

Pablo Barros, University of Hamburg
Francisco Cruz, Universidad Central de Chile
German I. Parisi, University of Hamburg
Bruno Fernandes, Universidade de Pernambuco

See more details in:

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