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Models/Ensemble

Ensemble

Reported on 16 benchmarks across 4 tasks · 2 papers · 4 SOTA

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

Audio6 results

  • Audio captioningonClotho
    CIDEr· 2020-07-01
    0.319
    best: 14 (ZerAuCap)
    SOTA
    The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length EstimationarXiv:2007.00225
  • Audio captioningonClotho
    SPICE· 2020-07-01
    0.094
    best: 5.3 (ZerAuCap)
    SOTA
    The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length EstimationarXiv:2007.00225
  • Audio captioningonClotho
    SPIDEr· 2020-07-01
    0.207
    best: 9.7 (ZerAuCap)
    SOTA
    The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length EstimationarXiv:2007.00225
  • Audio captioningonClotho
    CIDEr· uses extra data
    0.4
    best: 14 (ZerAuCap)
  • Audio captioningonClotho
    SPICE· uses extra data
    0.137
    best: 5.3 (ZerAuCap)
  • Audio captioningonClotho
    SPIDEr· uses extra data
    0.318
    best: 9.7 (ZerAuCap)

Speech6 results

  • DialogueonVisual Dialog v1.0 test-std
    MRR (x 100)
    51.17
    best: 71.24 (MRR ensemble (Naive))
  • DialogueonVisual Dialog v1.0 test-std
    Mean
    6.69
    best: 49.61 (qqhe)
  • DialogueonVisual Dialog v1.0 test-std
    NDCG (x 100)
    75.35
    best: 78.7 (Single)
  • DialogueonVisual Dialog v1.0 test-std
    R@1
    38.9
    best: 58.3 (2 Step: Factor Graph Attention + VD-Bert)
  • DialogueonVisual Dialog v1.0 test-std
    R@10
    77.98
    best: 95.08 (Ensemble FGA + BERT)
  • DialogueonVisual Dialog v1.0 test-std
    R@5
    62.82
    best: 88.42 (Ensemble FGA + BERT)

Computer Vision6 results

  • Visual DialogonVisual Dialog v1.0 test-std
    MRR (x 100)
    51.17
    best: 71.24 (MRR ensemble (Naive))
  • Visual DialogonVisual Dialog v1.0 test-std
    Mean
    6.69
    best: 49.61 (qqhe)
  • Visual DialogonVisual Dialog v1.0 test-std
    NDCG (x 100)
    75.35
    best: 78.7 (Single)
  • Visual DialogonVisual Dialog v1.0 test-std
    R@1
    38.9
    best: 58.3 (2 Step: Factor Graph Attention + VD-Bert)
  • Visual DialogonVisual Dialog v1.0 test-std
    R@10
    77.98
    best: 95.08 (Ensemble FGA + BERT)
  • Visual DialogonVisual Dialog v1.0 test-std
    R@5
    62.82
    best: 88.42 (Ensemble FGA + BERT)

Medical1 result

  • SkinonISIC 2019
    Accuracy· 2021-01-11
    0.634
    SOTA
    Analysis of skin lesion images with deep learningarXiv:2101.03814