# Improving Personalisation in Valence and Arousal Prediction using Data Augmentation

arXiv: https://arxiv.org/abs/2404.09042 · HTML: https://mlsanity.com/paper/2404.09042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 81 references that are in the map.
- First posted on arXiv 13 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy](https://mlsanity.com/paper/1802.01186/) (2018)
- [The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress](https://mlsanity.com/paper/2104.07123/) (2021)
- [Personalization of Deep Learning](https://mlsanity.com/paper/1909.02803/) (2019)
- [Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention](https://mlsanity.com/paper/2209.09068/) (2022)
- [The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation](https://mlsanity.com/paper/2305.03369/) (2023)
- [Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models](https://mlsanity.com/paper/2305.19184/) (2023)
- [CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition](https://mlsanity.com/paper/2208.07221/) (2022)
- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals](https://mlsanity.com/paper/1711.06116/) (2017)
- [Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition](https://mlsanity.com/paper/2007.05932/) (2020)
- [Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals](https://mlsanity.com/paper/1808.06774/) (2018)
- [Block-Segmentation Vectors for Arousal Prediction using Semi-supervised Learning](https://mlsanity.com/paper/2204.05096/) (2022)

## Built on by (most cited first, 1 of 1)

- [Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS](https://mlsanity.com/paper/2610.02796/) (2026)

## Abstract (arXiv)

In the field of emotion recognition and Human-Machine Interaction (HMI), personalised approaches have exhibited their efficacy in capturing individual-specific characteristics and enhancing affective prediction accuracy. However, personalisation techniques often face the challenge of limited data for target individuals. This paper presents our work on an enhanced personalisation strategy, that leverages data augmentation to develop tailored models for continuous valence and arousal prediction. Our proposed approach, Distance Weighting Augmentation (DWA), employs a weighting-based augmentation method that expands a target individual's dataset, leveraging distance metrics to identify similar samples at the segment-level. Experimental results on the MuSe-Personalisation 2023 Challenge dataset demonstrate that our method significantly improves the performance of features sets which have low baseline performance, on the test set. This improvement in poor-performing features comes without sacrificing performance on high-performing features. In particular, our method achieves a maximum combined testing CCC of 0.78, compared to the reported baseline score of 0.76 (reproduced at 0.72). It also achieved a peak arousal and valence scores of 0.81 and 0.76, compared to reproduced baseline scores of 0.76 and 0.67 respectively. Through this work, we make significant contributions to the advancement of personalised affective computing models, enhancing the practicality and adaptability of data-level personalisation in real world contexts.