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Data augmentation is a prevalent technique for improving performance invarious machine learning applications. We propose SeqAug, a modality-agnosticaugmentation method that is tailored towards sequences of extracted features.The core idea of SeqAug is to augment the sequence by resampling from theunderlying feature distribution. Resampling is performed by randomly selectingfeature dimensions and permuting them along the temporal axis. Experiments onCMU-MOSEI verify that SeqAug is modality agnostic; it can be successfullyapplied to a single modality or multiple modalities. We further verify itscompatibility with both recurrent and transformer architectures, and alsodemonstrate comparable to state-of-the-art results.

SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method