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WebVision Database: Visual Learning and Understanding from Web Data

arXiv 1708.02862 ↗First posted 9 Aug 2017cs.CVcited by 262

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Abstract (arXiv)

In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than 2.4 million web images crawled from the Internet by using queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012 dataset. Meta information along with those web images (e.g., title, description, tags, etc.) are also crawled. A validation set and test set containing human annotated images are also provided to facilitate algorithmic development. Based on our new database, we obtain a few interesting observations: 1) the noisy web images are sufficient for training a good deep CNN model for visual recognition; 2) the model learnt from our WebVision database exhibits comparable or even better generalization ability than the one trained from the ILSVRC 2012 dataset when being transferred to new datasets and tasks; 3) a domain adaptation issue (a.k.a., dataset bias) is observed, which means the dataset can be used as the largest benchmark dataset for visual domain adaptation. Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data.

Key facts. Cited by 262 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 64; 2022: 52; 2023: 65; 2024: 39; 2025: 24; 2026: 18.

262 papers.

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  1. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
  2. 2023Composer: Creative and Controllable Image Synthesis with Composable Conditionscited by 303
  3. 2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
  4. 2021RedCaps: web-curated image-text data created by the people, for the peoplecited by 175
  5. 2021Efficient Self-supervised Vision Transformers for Representation Learningcited by 149
  6. 2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
  7. 2021Provably End-to-end Label-Noise Learning without Anchor Pointscited by 97
  8. 2022Robust Training under Label Noise by Over-parameterizationcited by 93
  9. 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
  10. 2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
  11. 2023Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Followingcited by 77
  12. 2021Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labelscited by 74
38 more
  1. 2021Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labelscited by 69
  2. 2021Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environmentscited by 65
  3. 2022Will Large-scale Generative Models Corrupt Future Datasets?cited by 59
  4. 2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
  5. 2024CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generationcited by 58
  6. 2022Learning with Neighbor Consistency for Noisy Labelscited by 55
  7. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  8. 2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
  9. 2023Class-Balancing Diffusion Modelscited by 47
  10. 2021Asymmetric Loss Functions for Learning with Noisy Labelscited by 46
  11. 2022Investigating Why Contrastive Learning Benefits Robustness Against Label Noisecited by 46
  12. 2023When Source-Free Domain Adaptation Meets Learning with Noisy Labelscited by 46
  13. 2022Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimationcited by 43
  14. 2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
  15. 2022Mitigating Memorization of Noisy Labels by Clipping the Model Predictioncited by 40
  16. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 39
  17. 2021Faster Meta Update Strategy for Noise-Robust Deep Learningcited by 38
  18. 2021Learning with Noisy Labels via Sparse Regularizationcited by 38
  19. 2021Correlated Input-Dependent Label Noise in Large-Scale Image Classificationcited by 37
  20. 2021Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterioncited by 36
  21. 2021Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directionscited by 35
  22. 2021Robust Long-Tailed Learning under Label Noisecited by 35
  23. 2023A Path to Simpler Models Starts With Noisecited by 33
  24. 2021Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approachcited by 32
  25. 2021Boosting Co-teaching with Compression Regularization for Label Noisecited by 31
  26. 2022Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundariescited by 31
  27. 2023Twin Contrastive Learning with Noisy Labelscited by 28
  28. 2021Continual Learning on Noisy Data Streams via Self-Purified Replaycited by 27
  29. 2025Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Modelscited by 27
  30. 2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
  31. 2022SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labelscited by 24
  32. 2023Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidancecited by 24
  33. 2022Neighborhood Collective Estimation for Noisy Label Identification and Correctioncited by 22
  34. 2022When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Methodcited by 21
  35. 2023Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Modelscited by 21
  36. 2025Early Stopping Against Label Noise Without Validation Datacited by 21
  37. 2022FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentationcited by 20
  38. 2023Improving Image Recognition by Retrieving from Web-Scale Image-Text Datacited by 20

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