  Humans are emotional creatures. Multiple modalities are often involved when
we express emotions, whether we do so explicitly (e.g., facial expression,
speech) or implicitly (e.g., text, image). Enabling machines to have emotional
intelligence, i.e., recognizing, interpreting, processing, and simulating
emotions, is becoming increasingly important. In this tutorial, we discuss
several key aspects of multi-modal emotion recognition (MER). We begin with a
brief introduction on widely used emotion representation models and affective
modalities. We then summarize existing emotion annotation strategies and
corresponding computational tasks, followed by the description of main
challenges in MER. Furthermore, we present some representative approaches on
representation learning of each affective modality, feature fusion of different
affective modalities, classifier optimization for MER, and domain adaptation
for MER. Finally, we outline several real-world applications and discuss some
future directions.
