# SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method

arXiv: https://arxiv.org/abs/2305.01954 · HTML: https://mlsanity.com/paper/2305.01954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach](https://mlsanity.com/paper/1812.05040/) (2018)
- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations](https://mlsanity.com/paper/1606.01305/) (2016)
- [M-SENA: An Integrated Platform for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2203.12441/) (2022)
- [Learning Multimodal VAEs through Mutual Supervision](https://mlsanity.com/paper/2106.12570/) (2021)
- [A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments](https://mlsanity.com/paper/2202.08325/) (2022)
- [MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2201.09828/) (2022)
- [MMGAN: Generative Adversarial Networks for Multi-Modal Distributions](https://mlsanity.com/paper/1911.06663/) (2019)

## Built on by (most cited first, 3 of 3)

- [DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis](https://mlsanity.com/paper/2504.11082/) (2025)
- [Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025](https://mlsanity.com/paper/2506.02088/) (2025)
- [Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis](https://mlsanity.com/paper/2607.12686/) (2026)

## Abstract (arXiv)

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.