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Models/Sample4Geo

Sample4Geo

Reported on 28 benchmarks across 5 tasks · 2 papers · 24 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision29 results

  • Object Localizationoncvusa
    Recall@1· 2023-03-21
    98.68
    best: 99.19 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvusa
    Recall@10· 2023-03-21
    99.78
    best: 99.85 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvusa
    Recall@5· 2023-03-21
    99.68
    best: 99.8 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvusa
    Recall@top1%· 2023-03-21
    99.87
    best: 99.92 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvact
    Recall@1· 2023-03-21
    90.81
    best: 92.59 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvact
    Recall@1 (%)· 2023-03-21
    98.77
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvact
    Recall@10· 2023-03-21
    97.48
    best: 97.82 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object Localizationoncvact
    Recall@5· 2023-03-21
    96.74
    best: 97.16 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Cross Area
    Hit Rate· 2023-03-21
    69.87
    best: 75.97 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Cross Area
    Recall@1· 2023-03-21
    61.7
    best: 64.61 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Cross Area
    Recall@1%· 2023-03-21
    98.17
    best: 98.63 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Cross Area
    Recall@10· 2023-03-21
    88
    best: 91.2 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Cross Area
    Recall@5· 2023-03-21
    83.5
    best: 87.48 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Same Area
    Hit Rate· 2023-03-21
    89.82
    best: 90.76 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Same Area
    Recall@1· 2023-03-21
    77.86
    best: 78.27 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Same Area
    Recall@10· 2023-03-21
    97.21
    best: 97.52 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Object LocalizationonVIGOR Same Area
    Recall@5· 2023-03-21
    95.66
    best: 96.1 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Camera LocalizationonSpaGBOL
    Top-1· 2023-03-21
    50.8
    best: 56.48 (SpaGBOL)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Image RetrievalonUniversity-1652
    AP· 2023-03-21
    93.81
    best: 96.88 (Orientation-Guided Sample4Geo)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Image RetrievalonUniversity-1652
    Recall@1· 2023-03-21
    92.65
    best: 97.43 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Visual Place RecognitiononCV-Cities
    Recall@1· 2023-03-21
    74.49
    best: 82.91 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Visual Place RecognitiononCV-Cities
    Recall@5· 2023-03-21
    84.07
    best: 90.14 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Content-Based Image RetrievalonUniversity-1652
    AP· 2023-03-21
    93.81
    best: 96.88 (Orientation-Guided Sample4Geo)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Content-Based Image RetrievalonUniversity-1652
    Recall@1· 2023-03-21
    92.65
    best: 97.43 (CV-Cities)
    SOTA
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Camera LocalizationonSpaGBOL
    Top-1%· 2024-09-23
    82.32
    best: 87.24 (SpaGBOL)
    SpaGBOL: Spatial-Graph-Based Orientated LocalisationarXiv:2409.15514
  • Camera LocalizationonSpaGBOL
    Top-10· 2024-09-23
    79.96
    best: 83.85 (SpaGBOL)
    SpaGBOL: Spatial-Graph-Based Orientated LocalisationarXiv:2409.15514
  • Camera LocalizationonSpaGBOL
    Top-5· 2024-09-23
    74.22
    best: 77.47 (SpaGBOL)
    SpaGBOL: Spatial-Graph-Based Orientated LocalisationarXiv:2409.15514
  • Object LocalizationonVIGOR Same Area
    Recall@1%· 2023-03-21
    99.61
    best: 99.68 (SAIG-D)
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851
  • Camera LocalizationonSpaGBOL
    Top-1· 2023-03-21
    50.8
    best: 56.48 (SpaGBOL)
    Sample4Geo: Hard Negative Sampling For Cross-View Geo-LocalisationarXiv:2303.11851