  While the incipient internet was largely text-based, the modern digital world
is becoming increasingly multi-modal. Here, we examine multi-modal
classification where one modality is discrete, e.g. text, and the other is
continuous, e.g. visual representations transferred from a convolutional neural
network. In particular, we focus on scenarios where we have to be able to
classify large quantities of data quickly. We investigate various methods for
performing multi-modal fusion and analyze their trade-offs in terms of
classification accuracy and computational efficiency. Our findings indicate
that the inclusion of continuous information improves performance over
text-only on a range of multi-modal classification tasks, even with simple
fusion methods. In addition, we experiment with discretizing the continuous
features in order to speed up and simplify the fusion process even further. Our
results show that fusion with discretized features outperforms text-only
classification, at a fraction of the computational cost of full multi-modal
fusion, with the additional benefit of improved interpretability.
