# CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition

arXiv: https://arxiv.org/abs/2210.17233 · HTML: https://mlsanity.com/paper/2210.17233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition](https://mlsanity.com/paper/1811.07770/) (2018)
- [JhatAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention](https://mlsanity.com/paper/2003.08834/) (2020)
- [Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition](https://mlsanity.com/paper/1904.09939/) (2019)
- [Deep Structure Inference Network for Facial Action Unit Recognition](https://mlsanity.com/paper/1803.05873/) (2018)
- [Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey](https://mlsanity.com/paper/2005.10691/) (2020)
- [Expressive Explanations of DNNs by Combining Concept Analysis with ILP](https://mlsanity.com/paper/2105.07371/) (2021)
- [Explanation as a process: user-centric construction of multi-level and multi-modal explanations](https://mlsanity.com/paper/2110.03759/) (2021)
- [Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection](https://mlsanity.com/paper/2003.08751/) (2020)
- [CC-Loss: Channel Correlation Loss For Image Classification](https://mlsanity.com/paper/2010.05469/) (2020)

## Abstract (arXiv)

Neural networks are widely adopted, yet the integration of domain knowledge is still underutilized. We propose to integrate domain knowledge about co-occurring facial movements as a constraint in the loss function to enhance the training of neural networks for affect recognition. As the co-ccurrence patterns tend to be similar across datasets, applying our method can lead to a higher generalizability of models and a lower risk of overfitting. We demonstrate this by showing performance increases in cross-dataset testing for various datasets. We also show the applicability of our method for calibrating neural networks to different facial expressions.