  The focal point of egocentric video understanding is modelling hand-object
interactions. Standard models, e.g. CNNs or Vision Transformers, which receive
RGB frames as input perform well. However, their performance improves further
by employing additional input modalities that provide complementary cues, such
as object detections, optical flow, audio, etc. The added complexity of the
modality-specific modules, on the other hand, makes these models impractical
for deployment. The goal of this work is to retain the performance of such a
multimodal approach, while using only the RGB frames as input at inference
time. We demonstrate that for egocentric action recognition on the
Epic-Kitchens and the Something-Something datasets, students which are taught
by multimodal teachers tend to be more accurate and better calibrated than
architecturally equivalent models trained on ground truth labels in a unimodal
or multimodal fashion. We further adopt a principled multimodal knowledge
distillation framework, allowing us to deal with issues which occur when
applying multimodal knowledge distillation in a naive manner. Lastly, we
demonstrate the achieved reduction in computational complexity, and show that
our approach maintains higher performance with the reduction of the number of
input views. We release our code at
https://github.com/gorjanradevski/multimodal-distillation.
