  Consider end-to-end training of a multi-modal vs. a single-modal network on a
task with multiple input modalities: the multi-modal network receives more
information, so it should match or outperform its single-modal counterpart. In
our experiments, however, we observe the opposite: the best single-modal
network always outperforms the multi-modal network. This observation is
consistent across different combinations of modalities and on different tasks
and benchmarks.
  This paper identifies two main causes for this performance drop: first,
multi-modal networks are often prone to overfitting due to increased capacity.
Second, different modalities overfit and generalize at different rates, so
training them jointly with a single optimization strategy is sub-optimal. We
address these two problems with a technique we call Gradient Blending, which
computes an optimal blend of modalities based on their overfitting behavior. We
demonstrate that Gradient Blending outperforms widely-used baselines for
avoiding overfitting and achieves state-of-the-art accuracy on various tasks
including human action recognition, ego-centric action recognition, and
acoustic event detection.
