  Learning multi-modal representations is an essential step towards real-world
robotic applications, and various multi-modal fusion models have been developed
for this purpose. However, we observe that existing models, whose objectives
are mostly based on joint training, often suffer from learning inferior
representations of each modality. We name this problem Modality Failure, and
hypothesize that the imbalance of modalities and the implicit bias of common
objectives in fusion method prevent encoders of each modality from sufficient
feature learning. To this end, we propose a new multi-modal learning method,
Uni-Modal Teacher, which combines the fusion objective and uni-modal
distillation to tackle the modality failure problem. We show that our method
not only drastically improves the representation of each modality, but also
improves the overall multi-modal task performance. Our method can be
effectively generalized to most multi-modal fusion approaches. We achieve more
than 3% improvement on the VGGSound audio-visual classification task, as well
as improving performance on the NYU depth V2 RGB-D image segmentation task.
