mlsanityData to 7 Oct 2026

ImageNet Large Scale Visual Recognition Challenge

arXiv 1409.0575 ↗First posted 1 Sep 2014cs.CVcited by 8,441

Not on the map. Why?

Cited by 361 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.

Key facts. Cited by 8,441 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2014.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2017: 1; 2018: 2; 2019: 3; 2020: 1; 2021: 1787; 2022: 1651; 2023: 1686; 2024: 1457; 2025: 1147; 2026: 706.

8,441 papers.

Sort:

By area:

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  4. 2022Classifier-Free Diffusion Guidancecited by 5,717
  5. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  6. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  7. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  8. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  9. 2022A ConvNet for the 2020scited by 3,545
  10. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
  11. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  12. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
38 more
  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
  3. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  4. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  5. 2024Depth Anything V2cited by 1,385
  6. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
  7. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
  8. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,224
  9. 2021Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Conceptscited by 1,209
  10. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,162
  11. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  12. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  13. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  14. 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,058
  15. 2021Scaling Vision Transformerscited by 993
  16. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 982
  17. 2023Vision Transformers Need Registerscited by 844
  18. 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMscited by 822
  19. 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
  20. 2021Going deeper with Image Transformerscited by 759
  21. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
  22. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
  23. 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 722
  24. 2022On Distillation of Guided Diffusion Modelscited by 721
  25. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMscited by 719
  26. 2024SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformerscited by 707
  27. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  28. 2021Do Vision Transformers See Like Convolutional Neural Networks?cited by 671
  29. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 657
  30. 2021FLAVA: A Foundational Language And Vision Alignment Modelcited by 646
  31. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  32. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
  33. 2023DataComp: In search of the next generation of multimodal datasetscited by 635
  34. 2021Transformer in Transformercited by 632
  35. 2021Merging Models with Fisher-Weighted Averagingcited by 613
  36. 2021ImageNet-21K Pretraining for the Massescited by 607
  37. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  38. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 562

50 most cited of 8,441 All 8,441 (JSON)