mlsanityData to 14 Oct 2026

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

arXiv 2104.00232 ↗First posted 1 Apr 2021cs.CVcited by 89

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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.

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

33 of 52 references.

References · 33

  1. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  2. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  3. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  4. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  5. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 737
  6. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 692
  7. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 653
  8. 2019Similarity-Preserving Knowledge Distillationcited by 516
  9. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 500
  10. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  11. 2019Confident Learning: Estimating Uncertainty in Dataset Labelscited by 413
  12. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 302
21 more
  1. 2019Probabilistic End-to-end Noise Correction for Learning with Noisy Labelscited by 210
  2. 2018Knowledge Distillation by On-the-Fly Native Ensemblecited by 186
  3. 2019Probabilistic Face Embeddingscited by 173
  4. 2016Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distributioncited by 158
  5. 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognitioncited by 157
  6. 2020Data Uncertainty Learning in Face Recognitioncited by 134
  7. 2019Adaptive Wing Loss for Robust Face Alignment via Heatmap Regressioncited by 128
  8. 2019Online Knowledge Distillation with Diverse Peerscited by 128
  9. 2019Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognitioncited by 127
  10. 2019Deep Self-Learning From Noisy Labelscited by 122
  11. 2019Image Classification with Deep Learning in the Presence of Noisy Labels: A Surveycited by 101
  12. 2020Uncertainty-aware Score Distribution Learning for Action Quality Assessmentcited by 80
  13. 2020Peer Collaborative Learning for Online Knowledge Distillationcited by 58
  14. 2019Frame attention networks for facial expression recognition in videoscited by 56
  15. 2018Emotion Recognition in Speech using Cross-Modal Transfer in the Wildcited by 49
  16. 2018Covariance Pooling For Facial Expression Recognitioncited by 37
  17. 2017Island Loss for Learning Discriminative Features in Facial Expression Recognitioncited by 35
  18. 2020TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Searchcited by 21
  19. 2019Noise-Tolerant Paradigm for Training Face Recognition CNNscited by 20
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  21. 2019Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 19; 2023: 23; 2024: 23; 2025: 13; 2026: 9.

89 papers.

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  1. 2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
  2. 2022POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognitioncited by 49
  3. 2023GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variationscited by 49
  4. 2022Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wildcited by 41
  5. 2023From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videoscited by 39
  6. 2024FaceXFormer: A Unified Transformer for Facial Analysiscited by 34
  7. 2022Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margincited by 32
  8. 2022Uncertainty-aware Label Distribution Learning for Facial Expression Recognitioncited by 30
  9. 2022NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognitioncited by 27
  10. 2023POSTER++: A simpler and stronger facial expression recognition networkcited by 25
  11. 2022COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognitioncited by 22
  12. 2021Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learningcited by 20
38 more
  1. 2024Affective Behaviour Analysis via Integrating Multi-Modal Knowledgecited by 18
  2. 2022Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognitioncited by 17
  3. 2023LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noisecited by 17
  4. 2023Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognitioncited by 17
  5. 2023Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understandingcited by 16
  6. 2023CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimationcited by 15
  7. 2024ExpLLM: Towards Chain of Thought for Facial Expression Recognitioncited by 15
  8. 2023ReSup: Reliable Label Noise Suppression for Facial Expression Recognitioncited by 14
  9. 2024Representation Learning and Identity Adversarial Training for Facial Behavior Understandingcited by 14
  10. 2024Faceptor: A Generalist Model for Face Perceptioncited by 13
  11. 2023Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotioncited by 12
  12. 2023VEATIC: Video-based Emotion and Affect Tracking in Context Datasetcited by 12
  13. 2022Disentangling Identity and Pose for Facial Expression Recognitioncited by 11
  14. 2022Uncertain Facial Expression Recognition via Multi-task Assisted Correctioncited by 10
  15. 2021Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusioncited by 9
  16. 2022Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformerscited by 9
  17. 2023ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learningcited by 9
  18. 2024AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labelscited by 9
  19. 2025Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Datacited by 9
  20. 2022Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognitioncited by 8
  21. 2023FER-former: Multi-modal Transformer for Facial Expression Recognitioncited by 8
  22. 2024Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modelingcited by 8
  23. 2024Affective Behaviour Analysis via Progressive Learningcited by 7
  24. 2022Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognitioncited by 6
  25. 2024LRDif: Diffusion Models for Under-Display Camera Emotion Recognitioncited by 6
  26. 2025Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perceptioncited by 6
  27. 2024Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformercited by 5
  28. 2022Privileged Attribution Constrained Deep Networks for Facial Expression Recognitioncited by 4
  29. 2022AU-Aware Vision Transformers for Biased Facial Expression Recognitioncited by 4
  30. 2023Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognitioncited by 4
  31. 2023Unsupervised Facial Expression Representation Learning with Contrastive Local Warpingcited by 4
  32. 2023NeutrEx: A 3D Quality Component Measure on Facial Expression Neutralitycited by 4
  33. 2023Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learningcited by 4
  34. 2025MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognitioncited by 4
  35. 2022Rethinking the Learning Paradigm for Facial Expression Recognitioncited by 3
  36. 2024Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformationcited by 3
  37. 2024QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognitioncited by 3
  38. 2025Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyondcited by 3

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