# Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition

arXiv: https://arxiv.org/abs/2404.15714 · HTML: https://mlsanity.com/paper/2404.15714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://mlsanity.com/paper/2104.00232/) (2021)
- [Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://mlsanity.com/paper/2207.10299/) (2022)
- [Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition](https://mlsanity.com/paper/2007.01755/) (2020)
- [Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin](https://mlsanity.com/paper/2203.12341/) (2022)
- [Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition](https://mlsanity.com/paper/2005.06040/) (2020)
- [ReSup: Reliable Label Noise Suppression for Facial Expression Recognition](https://mlsanity.com/paper/2305.17895/) (2023)
- ["Excavating AI" Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset](https://mlsanity.com/paper/2107.13998/) (2021)
- [Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher](https://mlsanity.com/paper/2205.14361/) (2022)
- [Disentangling Identity and Pose for Facial Expression Recognition](https://mlsanity.com/paper/2208.08106/) (2022)
- [Unsupervised Facial Expression Representation Learning with Contrastive Local Warping](https://mlsanity.com/paper/2303.09034/) (2023)

## Abstract (arXiv)

Facial expression recognition (FER) plays a significant role in our daily life. However, annotation ambiguity in the datasets could greatly hinder the performance. In this paper, we address FER task via label distribution learning paradigm, and develop a dual-branch Adaptive Distribution Fusion (Ada-DF) framework. One auxiliary branch is constructed to obtain the label distributions of samples. The class distributions of emotions are then computed through the label distributions of each emotion. Finally, those two distributions are adaptively fused according to the attention weights to train the target branch. Extensive experiments are conducted on three real-world datasets, RAF-DB, AffectNet and SFEW, where our Ada-DF shows advantages over the state-of-the-art works.