The OMG-Emotion Behavior Dataset

Barros, Pablo; Churamani, Nikhil; Lakomkin, Egor; Siqueira, Henrique; Sutherland, Alexander; Wermter, Stefan

doi:10.1109/ijcnn.2018.8489099

Cited by 94 publications

(86 citation statements)

References 20 publications

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“…Table 3 presents the performance of the models in terms of F1 on the development set (note that the annotations of the test set are not publicly available). From the table, we can see that on this database, our classic monomodal models outperform the other methods reported in the literature [47], i. e., Support Vector Machine (SVM) and Random Forest (RF). More specifically, our classic monomodal models yields higher F1 than SVM (36.5 % vs 33.0 %) for audio, and than RF (37.9 % vs 37.0 %) for video.…”

Section: Results On Omg-emotionmentioning

confidence: 75%

EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

Ren

Schuller

2021

IEEE Trans. Affective Comput.

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Despite remarkable advances in emotion recognition, they are severely restrained from either the essentially limited property of the employed single modality, or the synchronous presence of all involved multiple modalities. Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand. The framework generally includes two main learning components, i. e., joint multimodal training and crossmodal training. Both of them tend to explore the underlying semantic emotion information but with a shared recognition network or with a shared emotion embedding space, respectively. In doing this, the enhanced system trained with this approach can efficiently make use of the complementary information from other modalities. Nevertheless, the presence of these auxiliary modalities is not demanded during inference. To empirically investigate the effectiveness and robustness of the proposed framework, we perform extensive experiments on the two benchmark databases RECOLA and OMG-Emotion for the tasks of dimensional emotion regression and categorical emotion classification, respectively. The obtained results show that the proposed framework significantly outperforms related baselines in monomodal inference, and are also competitive or superior to the recently reported systems, which emphasises the importance of the proposed crossmodal learning for emotion recognition.

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Section: Results On Omg-emotionmentioning

confidence: 75%

EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

Ren

Schuller

2021

IEEE Trans. Affective Comput.

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“…To annotate the videos, the listeners used a modified version of the KT-Annotation Tool [20] which was designed as a dynamic tool for collecting dataset annotations. The tool provides annotators with a web-based system that can be adjusted for different annotation scenarios.…”

Section: B Self-assessment Annotationmentioning

confidence: 99%

The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling

Barros

Churamani

Lim

et al. 2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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Processing human affective behavior is important for developing intelligent agents that interact with humans in complex interaction scenarios. A large number of current approaches that address this problem focus on classifying emotion expressions by grouping them into known categories. Such strategies neglect, among other aspects, the impact of the affective responses from an individual on their interaction partner thus ignoring how people empathize towards each other. This is also reflected in the datasets used to train models for affective processing tasks. Most of the recent datasets, in particular, the ones which capture natural interactions ("in-the-wild" datasets), are designed, collected, and annotated based on the recognition of displayed affective reactions, ignoring how these displayed or expressed emotions are perceived. In this paper, we propose a novel dataset composed of dyadic interactions designed, collected and annotated with a focus on measuring the affective impact that eight different stories have on the listener. Each video of the dataset contains around 5 minutes of interaction where a speaker tells a story to a listener. After each interaction, the listener annotated, using a valence scale, how the story impacted their affective state, reflecting how they empathized with the speaker as well as the story. We also propose different evaluation protocols and a baseline that encourages participation in the advancement of the field of artificial empathy and emotion contagion.

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“…Improving Happy cluster centroid proximity through (9) will discretize the 2D Valence Arousal Space, e.g. in 400 (20 × 20) classes and then apply the proposed approach between different in-the-wild datasets, such as AffWild, RECOLA [20] and OMG-Emotion Behavior Dataset [1].…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm

Kollias

Zafeiriou

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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A novel procedure is presented in this paper, for training a deep convolutional and recurrent neural network, taking into account both the available training data set and some information extracted from similar networks trained with other relevant data sets. This information is included in an extended loss function used for the network training, so that the network can have an improved performance when applied to the other data sets, without forgetting the learned knowledge from the original data set. Facial expression and emotion recognition inthe-wild is the test bed application that is used to demonstrate the improved performance achieved using the proposed approach. In this framework, we provide an experimental study on categorical emotion recognition using datasets from a very recent related emotion recognition challenge.

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The OMG-Emotion Behavior Dataset

Cited by 94 publications

References 20 publications

EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling

Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm

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