  Emotion recognition is involved in several real-world applications. With an
increase in available modalities, automatic understanding of emotions is being
performed more accurately. The success in Multimodal Emotion Recognition (MER),
primarily relies on the supervised learning paradigm. However, data annotation
is expensive, time-consuming, and as emotion expression and perception depends
on several factors (e.g., age, gender, culture) obtaining labels with a high
reliability is hard. Motivated by these, we focus on unsupervised feature
learning for MER. We consider discrete emotions, and as modalities text, audio
and vision are used. Our method, as being based on contrastive loss between
pairwise modalities, is the first attempt in MER literature. Our end-to-end
feature learning approach has several differences (and advantages) compared to
existing MER methods: i) it is unsupervised, so the learning is lack of data
labelling cost; ii) it does not require data spatial augmentation, modality
alignment, large number of batch size or epochs; iii) it applies data fusion
only at inference; and iv) it does not require backbones pre-trained on emotion
recognition task. The experiments on benchmark datasets show that our method
outperforms several baseline approaches and unsupervised learning methods
applied in MER. Particularly, it even surpasses a few supervised MER
state-of-the-art.
