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Models/CAN

CAN

Reported on 9 benchmarks across 4 tasks · 3 papers · 7 SOTA

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

Computer Vision6 results

  • CrowdsonShanghaiTech B
    MAE· 2018-11-26
    7.8
    best: 5.51 (EBC-ZIP-B)
    SOTA
    Context-Aware Crowd CountingarXiv:1811.10452
  • CrowdsonUCF-QNRF
    MAE· 2018-11-26
    107
    best: 69.46 (EBC-ZIP-B)
    SOTA
    Context-Aware Crowd CountingarXiv:1811.10452
  • CrowdsonShanghaiTech A
    MAE· 2018-11-26
    62.3
    best: 47.81 (EBC-ZIP-B)
    SOTA
    Context-Aware Crowd CountingarXiv:1811.10452
  • CrowdsonVenice
    MAE· 2018-11-26
    23.5
    best: 20.5 (ECAN)
    SOTA
    Context-Aware Crowd CountingarXiv:1811.10452
  • CrowdsonUCF CC 50
    MAE· 2018-11-26
    212.2
    best: 154.8 (APGCC)
    SOTA
    Context-Aware Crowd CountingarXiv:1811.10452
  • CrowdsonWorldExpo’10
    Average MAE· 2018-11-26
    7.4
    best: 7.2 (ECAN)
    SOTA
    Context-Aware Crowd CountingarXiv:1811.10452

Methodology2 results

  • Domain AdaptationonVisDA2017
    Accuracy· 2019-01-04
    87.2
    best: 93.8 (FFTAT)
    SOTA
    Contrastive Adaptation Network for Unsupervised Domain AdaptationarXiv:1901.00976
  • ClassificationonNCI109
    Accuracy· 2022-09-16
    83.6
    best: 87.3 (WKPI-kcenters)
    Cell Attention NetworksarXiv:2209.08179

Graphs1 result

  • Graph ClassificationonNCI109
    Accuracy· 2022-09-16
    83.6
    best: 87.3 (WKPI-kcenters)
    Cell Attention NetworksarXiv:2209.08179