# Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2104.00232 · HTML: https://mlsanity.com/paper/2104.00232/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 52 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 30 of 33)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Similarity-Preserving Knowledge Distillation](https://mlsanity.com/paper/1907.09682/) (2019)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Probabilistic End-to-end Noise Correction for Learning with Noisy Labels](https://mlsanity.com/paper/1903.07788/) (2019)
- [Knowledge Distillation by On-the-Fly Native Ensemble](https://mlsanity.com/paper/1806.04606/) (2018)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Online Knowledge Distillation with Diverse Peers](https://mlsanity.com/paper/1912.00350/) (2019)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [Deep Self-Learning From Noisy Labels](https://mlsanity.com/paper/1908.02160/) (2019)
- [Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey](https://mlsanity.com/paper/1912.05170/) (2019)
- [Uncertainty-aware Score Distribution Learning for Action Quality Assessment](https://mlsanity.com/paper/2006.07665/) (2020)
- [Peer Collaborative Learning for Online Knowledge Distillation](https://mlsanity.com/paper/2006.04147/) (2020)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [Emotion Recognition in Speech using Cross-Modal Transfer in the Wild](https://mlsanity.com/paper/1808.05561/) (2018)
- [Covariance Pooling For Facial Expression Recognition](https://mlsanity.com/paper/1805.04855/) (2018)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://mlsanity.com/paper/1710.03144/) (2017)
- [TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search](https://mlsanity.com/paper/2008.05314/) (2020)

## Built on by (most cited first, 30 of 89)

- [Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://mlsanity.com/paper/2207.10299/) (2022)
- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations](https://mlsanity.com/paper/2305.19962/) (2023)
- [Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2208.10335/) (2022)
- [From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2312.05447/) (2023)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin](https://mlsanity.com/paper/2203.12341/) (2022)
- [Uncertainty-aware Label Distribution Learning for Facial Expression Recognition](https://mlsanity.com/paper/2209.10448/) (2022)
- [NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2206.04975/) (2022)
- [POSTER++: A simpler and stronger facial expression recognition network](https://mlsanity.com/paper/2301.12149/) (2023)
- [COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition](https://mlsanity.com/paper/2206.05833/) (2022)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [Affective Behaviour Analysis via Integrating Multi-Modal Knowledge](https://mlsanity.com/paper/2403.10825/) (2024)
- [Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition](https://mlsanity.com/paper/2203.13052/) (2022)
- [LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise](https://mlsanity.com/paper/2307.09023/) (2023)
- [Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition](https://mlsanity.com/paper/2310.19636/) (2023)
- [Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding](https://mlsanity.com/paper/2304.00058/) (2023)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [ExpLLM: Towards Chain of Thought for Facial Expression Recognition](https://mlsanity.com/paper/2409.02828/) (2024)
- [ReSup: Reliable Label Noise Suppression for Facial Expression Recognition](https://mlsanity.com/paper/2305.17895/) (2023)
- [Representation Learning and Identity Adversarial Training for Facial Behavior Understanding](https://mlsanity.com/paper/2407.11243/) (2024)
- [Faceptor: A Generalist Model for Face Perception](https://mlsanity.com/paper/2403.09500/) (2024)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Disentangling Identity and Pose for Facial Expression Recognition](https://mlsanity.com/paper/2208.08106/) (2022)
- [Uncertain Facial Expression Recognition via Multi-task Assisted Correction](https://mlsanity.com/paper/2212.07144/) (2022)
- [Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion](https://mlsanity.com/paper/2111.10561/) (2021)
- [Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers](https://mlsanity.com/paper/2207.11081/) (2022)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels](https://mlsanity.com/paper/2410.22506/) (2024)

## Abstract (arXiv)

Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity. In this paper, we proposes a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives: the latent Distribution Mining and the pairwise Uncertainty Estimation. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space. The proposed method is independent to the backbone architectures, and brings no extra burden for inference. The experiments are conducted on the popular real-world benchmarks and the synthetic noisy datasets. Either way, the proposed DMUE stably achieves leading performance.