# CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition

arXiv: https://arxiv.org/abs/2208.07221 · HTML: https://mlsanity.com/paper/2208.07221/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 23 of 23)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [TransFER: Learning Relation-aware Facial Expression Representations with Transformers](https://mlsanity.com/paper/2108.11116/) (2021)
- [Emotion Recognition in Speech using Cross-Modal Transfer in the Wild](https://mlsanity.com/paper/1808.05561/) (2018)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [The OMG-Emotion Behavior Dataset](https://mlsanity.com/paper/1803.05434/) (2018)
- [Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks](https://mlsanity.com/paper/2001.06338/) (2020)
- [Facial Expression Recognition using Convolutional Neural Networks: State of the Art](https://mlsanity.com/paper/1612.02903/) (2016)
- [Progressive Neural Networks for Transfer Learning in Emotion Recognition](https://mlsanity.com/paper/1706.03256/) (2017)
- [Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)](https://mlsanity.com/paper/2009.05938/) (2020)
- [Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity](https://mlsanity.com/paper/2005.05509/) (2020)
- [CNN-based Facial Affect Analysis on Mobile Devices](https://mlsanity.com/paper/1807.08775/) (2018)
- [Facial Expression and Peripheral Physiology Fusion to Decode Individualized Affective Experience](https://mlsanity.com/paper/1811.07392/) (2018)
- [A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction](https://mlsanity.com/paper/2007.14838/) (2020)
- [Person Re-identification Using Visual Attention](https://mlsanity.com/paper/1707.07336/) (2017)
- [A Personalized Affective Memory Neural Model for Improving Emotion Recognition](https://mlsanity.com/paper/1904.12632/) (2019)

## Built on by (most cited first, 4 of 4)

- [PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition](https://mlsanity.com/paper/2306.09626/) (2023)
- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [Improving Personalisation in Valence and Arousal Prediction using Data Augmentation](https://mlsanity.com/paper/2404.09042/) (2024)
- [I am Only Happy When There is Light: The Impact of Environmental Changes on Affective Facial Expressions Recognition](https://mlsanity.com/paper/2210.17421/) (2022)

## Abstract (arXiv)

Current facial expression recognition systems demand an expensive re-training routine when deployed to different scenarios than they were trained for. Biasing them towards learning specific facial characteristics, instead of performing typical transfer learning methods, might help these systems to maintain high performance in different tasks, but with a reduced training effort. In this paper, we propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations, in particular when compared with state-of-the-art models. In our discussions, we make an in-depth analysis of how the learned high-level facial features are represented, and how they contribute to each individual dataset's characteristics. We finalize our study by discussing how CIAO positions itself within the range of recent findings on non-universal facial expressions perception, and its impact on facial expression recognition research.