1 citations · 1 across the 4 of their papers we have counts for
4 papers
Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition
Juan Vazquez-Rodriguez, Grégoire Lefebvre, Julien Cumin +1
Decades of research indicate that emotion recognition is more effective when drawing information from multiple modalities. But what if some modalities are sometimes missing? To add…
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals
Juan Vazquez-Rodriguez, Grégoire Lefebvre, Julien Cumin +1
In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this t…
Transformer-Based Self-Supervised Learning for Emotion Recognition
Juan Vazquez-Rodriguez, Grégoire Lefebvre, Julien Cumin +1
In order to exploit representations of time-series signals, such as physiological signals, it is essential that these representations capture relevant information from the whole si…
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Yangtao Wang, Xi Shen, Shell Hu +3
Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this…