mlsanityData to 7 Oct 2026

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

arXiv 1803.11285 ↗First posted 29 Mar 2018cs.CVcited by 189

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Abstract (arXiv)

In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth. Three new protocols of varying difficulty are introduced. The protocols allow fair comparison between different methods, including those using a dataset pre-processing stage. For each dataset, 15 new challenging queries are introduced. Finally, a new set of 1M hard, semi-automatically cleaned distractors is selected. An extensive comparison of the state-of-the-art methods is performed on the new benchmark. Different types of methods are evaluated, ranging from local-feature-based to modern CNN based methods. The best results are achieved by taking the best of the two worlds. Most importantly, image retrieval appears far from being solved.

Key facts. Cited by 189 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Mar 2018.

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Citing papers per year: 2021: 38; 2022: 25; 2023: 39; 2024: 25; 2025: 29; 2026: 33.

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  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  3. 2023LightGlue: Local Feature Matching at Light Speedcited by 583
  4. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  5. 2021XCiT: Cross-Covariance Image Transformerscited by 335
  6. 2022A Self-Supervised Descriptor for Image Copy Detectioncited by 175
  7. 2023ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformationcited by 120
  8. 2022What to Hide from Your Students: Attention-Guided Masked Image Modelingcited by 117
  9. 2021Learning to Match Features with Seeded Graph Matching Networkcited by 92
  10. 2021Training Vision Transformers for Image Retrievalcited by 90
  11. 2023Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Taskscited by 72
  12. 2021Deep Learning for Instance Retrieval: A Surveycited by 70
38 more
  1. 2023GlueStick: Robust Image Matching by Sticking Points and Lines Togethercited by 65
  2. 2021Instance-level Image Retrieval using Reranking Transformerscited by 63
  3. 2022ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matchingcited by 62
  4. 2025MegaLoc: One Retrieval to Place Them Allcited by 58
  5. 2023SoftCLIP: Softer Cross-modal Alignment Makes CLIP Strongercited by 56
  6. 2024OmniGlue: Generalizable Feature Matching with Foundation Model Guidancecited by 55
  7. 2021DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Featurescited by 54
  8. 2021Recall@k Surrogate Loss with Large Batches and Similarity Mixupcited by 49
  9. 2024Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approachcited by 48
  10. 2022Correlation Verification for Image Retrievalcited by 47
  11. 2023GeneCIS: A Benchmark for General Conditional Image Similaritycited by 46
  12. 2022Teaching Matters: Investigating the Role of Supervision in Vision Transformerscited by 45
  13. 2021The 2021 Image Similarity Dataset and Challengecited by 44
  14. 2021One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objectivecited by 43
  15. 2023Data-efficient Large Scale Place Recognition with Graded Similarity Supervisioncited by 43
  16. 2025Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversioncited by 42
  17. 2023Global Features are All You Need for Image Retrieval and Rerankingcited by 41
  18. 2025MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Trainingcited by 40
  19. 2023Simple, Effective and General: A New Backbone for Cross-view Image Geo-localizationcited by 36
  20. 2022Learning Super-Features for Image Retrievalcited by 35
  21. 2025ILIAS: Instance-Level Image retrieval At Scalecited by 29
  22. 2023Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representationscited by 28
  23. 2021Class-Balanced Distillation for Long-Tailed Visual Recognitioncited by 27
  24. 2022Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmarkcited by 27
  25. 2021Noise-resistant Deep Metric Learning with Ranking-based Instance Selectioncited by 23
  26. 2022Towards Universal Backward-Compatible Representation Learningcited by 20
  27. 2021Robust and Decomposable Average Precision for Image Retrievalcited by 19
  28. 2022Boosting vision transformers for image retrievalcited by 19
  29. 2024Revisit Anything: Visual Place Recognition via Image Segment Retrievalcited by 19
  30. 2024Where's Waldo: Diffusion Features for Personalized Segmentation and Retrievalcited by 18
  31. 2022MimCo: Masked Image Modeling Pre-training with Contrastive Teachercited by 16
  32. 2023Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?cited by 16
  33. 2024AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-level Retrievalcited by 16
  34. 2025Hypergraph Vision Transformers: Images are More than Nodes, More than Edgescited by 16
  35. 2024On the Estimation of Image-matching Uncertainty in Visual Place Recognitioncited by 15
  36. 2021Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localizationcited by 14
  37. 2021Contextual Similarity Aggregation with Self-attention for Visual Re-rankingcited by 14
  38. 2021Self-Distilled Self-Supervised Representation Learningcited by 14

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