mlsanityData to 14 Oct 2026

Semantic Data Augmentation for Long-tailed Facial Expression Recognition

arXiv 2411.17254 ↗First posted 26 Nov 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expressions has been extensively studied by the Computer Vision research society. But Facial Expression Recognition in real-world is still a challenging task, partially due to the long-tailed distribution of the dataset. Many recent studies use data augmentation for Long-Tailed Recognition tasks. In this paper, we propose a novel semantic augmentation method. By introducing randomness into the encoding of the source data in the latent space of VAE-GAN, new samples are generated. Then, for facial expression recognition in RAF-DB dataset, we use our augmentation method to balance the long-tailed distribution. Our method can be used in not only FER tasks, but also more diverse data-hungry scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 26 Nov 2024.

Builds on

24 of 34 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  4. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  5. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  6. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  7. 2021Deep Long-Tailed Learning: A Surveycited by 417
  8. 2019Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishingcited by 191
  9. 2020Remix: Rebalanced Mixupcited by 144
  10. 2020Feature Space Augmentation for Long-Tailed Datacited by 136
  11. 2020M2m: Imbalanced Classification via Major-to-minor Translationcited by 134
  12. 2022Long-Tailed Recognition via Weight Balancingcited by 107
12 more
  1. 2021MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognitioncited by 99
  2. 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 97
  3. 2018BAGAN: Data Augmentation with Balancing GANcited by 90
  4. 2016Deep Feature Interpolation for Image Content Changescited by 80
  5. 2020Regularizing Deep Networks with Semantic Data Augmentationcited by 73
  6. 2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
  7. 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
  8. 2017Island Loss for Learning Discriminative Features in Facial Expression Recognitioncited by 35
  9. 2018DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GANcited by 34
  10. 2022MixAugment & Mixup: Augmentation Methods for Facial Expression Recognitioncited by 31
  11. 2016FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognitioncited by 25
  12. 2019Identity-Free Facial Expression Recognition using conditional Generative Adversarial Networkcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On Applicability of Synthetic Datasets for Facial Expression Recognitioncited by 0