  Data augmentation is a prevalent technique for improving performance in
various machine learning applications. We propose SeqAug, a modality-agnostic
augmentation method that is tailored towards sequences of extracted features.
The core idea of SeqAug is to augment the sequence by resampling from the
underlying feature distribution. Resampling is performed by randomly selecting
feature dimensions and permuting them along the temporal axis. Experiments on
CMU-MOSEI verify that SeqAug is modality agnostic; it can be successfully
applied to a single modality or multiple modalities. We further verify its
compatibility with both recurrent and transformer architectures, and also
demonstrate comparable to state-of-the-art results.
