mlsanityData to 7 Oct 2026

Large-Scale Long-Tailed Recognition in an Open World

arXiv 1904.05160 ↗First posted 10 Apr 2019cs.CV cs.LGcited by 704

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon a never seen instance. We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes. OLTR must handle imbalanced classification, few-shot learning, and open-set recognition in one integrated algorithm, whereas existing classification approaches focus only on one aspect and deliver poorly over the entire class spectrum. The key challenges are how to share visual knowledge between head and tail classes and how to reduce confusion between tail and open classes. We develop an integrated OLTR algorithm that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a direct image feature and an associated memory feature, with the feature norm indicating the familiarity to known classes. On three large-scale OLTR datasets we curate from object-centric ImageNet, scene-centric Places, and face-centric MS1M data, our method consistently outperforms the state-of-the-art. Our code, datasets, and models enable future OLTR research and are publicly available at https://liuziwei7.github.io/projects/LongTail.html.

Key facts. Cited by 704 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Apr 2019.

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Citing papers per year: 2021: 127; 2022: 154; 2023: 160; 2024: 115; 2025: 87; 2026: 61.

704 papers.

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  1. 2021Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Conceptscited by 1,209
  2. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  3. 2021Deep Long-Tailed Learning: A Surveycited by 416
  4. 2021Towards Open World Object Detectioncited by 297
  5. 2021IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoningcited by 262
  6. 2021Parametric Contrastive Learningcited by 217
  7. 2021Improving Calibration for Long-Tailed Recognitioncited by 212
  8. 2021Distribution Alignment: A Unified Framework for Long-tail Visual Recognitioncited by 198
  9. 2021CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learningcited by 174
  10. 2021Delving into Deep Imbalanced Regressioncited by 157
  11. 2022StyleGAN-Human: A Data-Centric Odyssey of Human Generationcited by 143
  12. 2022Balanced Contrastive Learning for Long-Tailed Visual Recognitioncited by 143
38 more
  1. 2021Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generationcited by 137
  2. 2021Fine-Grained Image Analysis with Deep Learning: A Surveycited by 135
  3. 2021Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentationcited by 133
  4. 2021Targeted Supervised Contrastive Learning for Long-Tailed Recognitioncited by 132
  5. 2021Contrastive Learning based Hybrid Networks for Long-Tailed Image Classificationcited by 130
  6. 2021Self-supervised Learning is More Robust to Dataset Imbalancecited by 122
  7. 2021Instance-Conditioned GANcited by 119
  8. 2023Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entitiescited by 112
  9. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  10. 2022Long-Tailed Recognition via Weight Balancingcited by 107
  11. 2021SynFace: Face Recognition with Synthetic Datacited by 106
  12. 2022Retrieval Augmented Classification for Long-Tail Visual Recognitioncited by 106
  13. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  14. 2021The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classificationcited by 100
  15. 2022Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?cited by 100
  16. 2021MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognitioncited by 99
  17. 2021ResLT: Residual Learning for Long-tailed Recognitioncited by 96
  18. 2021Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognitioncited by 95
  19. 2021Influence-Balanced Loss for Imbalanced Visual Classificationcited by 91
  20. 2022Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoningcited by 90
  21. 2022Balanced MSE for Imbalanced Visual Regressioncited by 89
  22. 2021ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shotcited by 86
  23. 2022Debiased Learning from Naturally Imbalanced Pseudo-Labelscited by 85
  24. 2022Temporal Action Segmentation: An Analysis of Modern Techniquescited by 85
  25. 2021Distributional Robustness Loss for Long-tail Learningcited by 84
  26. 2023Provable Dynamic Fusion for Low-Quality Multimodal Datacited by 81
  27. 2021Detecting Human-Object Interaction via Fabricated Compositional Learningcited by 79
  28. 2024DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignmentcited by 79
  29. 2022Generating High Fidelity Data from Low-density Regions using Diffusion Modelscited by 78
  30. 2023Generating images of rare concepts using pre-trained diffusion modelscited by 78
  31. 2024Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Modelscited by 78
  32. 2021FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentationcited by 76
  33. 2022A Survey on Long-Tailed Visual Recognitioncited by 75
  34. 2023Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustmentcited by 75
  35. 2021Label-Imbalanced and Group-Sensitive Classification under Overparameterizationcited by 74
  36. 2021Open-World Entity Segmentationcited by 74
  37. 2021Self Supervision to Distillation for Long-Tailed Visual Recognitioncited by 73
  38. 2021Distilling Virtual Examples for Long-tailed Recognitioncited by 72

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