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Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition
Decades of research indicate that emotion recognition is more effective when drawing information from multiple modalities.
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals
In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals.
Transformer-Based Self-Supervised Learning for Emotion Recognition
We gathered several ECG datasets with no labels of emotion to pre-train our model, which we then fine-tuned for emotion recognition on the AMIGOS dataset.
Sequence Metric Learning as Synchronization of Recurrent Neural Networks
Sequence metric learning is becoming a widely adopted approach for various applications dealing with sequential multi-variate data such as activity recognition or natural language processing and is most of the time tackled with sequence alignment approaches or representation learning.
Routine Modeling with Time Series Metric Learning
Traditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities.
