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Models/Baseline Model

Baseline Model

Reported on 23 benchmarks across 4 tasks · 2 papers · 1 SOTA

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

Computer Vision16 results

  • Intelligent SurveillanceonVRAI test
    CMC1
    0.81
  • Intelligent SurveillanceonVRAI test
    CMC10
    0.94
  • Intelligent SurveillanceonVRAI test
    CMC5
    0.9
  • Intelligent SurveillanceonVRAI test
    MAP
    0.79
  • Intelligent SurveillanceonVRAI test-dev
    CMC1
    0.8
  • Intelligent SurveillanceonVRAI test-dev
    CMC10
    0.95
  • Intelligent SurveillanceonVRAI test-dev
    CMC5
    0.89
  • Intelligent SurveillanceonVRAI test-dev
    MAP
    0.78
  • Vehicle Re-IdentificationonVRAI test
    CMC1
    0.81
  • Vehicle Re-IdentificationonVRAI test
    CMC10
    0.94
  • Vehicle Re-IdentificationonVRAI test
    CMC5
    0.9
  • Vehicle Re-IdentificationonVRAI test
    MAP
    0.79
  • Vehicle Re-IdentificationonVRAI test-dev
    CMC1
    0.8
  • Vehicle Re-IdentificationonVRAI test-dev
    CMC10
    0.95
  • Vehicle Re-IdentificationonVRAI test-dev
    CMC5
    0.89
  • Vehicle Re-IdentificationonVRAI test-dev
    MAP
    0.78

Natural Language Processing6 results

  • Question AnsweringonHotpotQA
    ANS-EM· 2018-09-25
    0.24
    best: 0.727 (Beam Retrieval)
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question AnsweringarXiv:1809.09600
  • Question AnsweringonHotpotQA
    ANS-F1· 2018-09-25
    0.329
    best: 0.85 (Beam Retrieval)
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question AnsweringarXiv:1809.09600
  • Question AnsweringonHotpotQA
    JOINT-EM· 2018-09-25
    0.019
    best: 0.505 (Beam Retrieval)
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question AnsweringarXiv:1809.09600
  • Question AnsweringonHotpotQA
    JOINT-F1· 2018-09-25
    0.162
    best: 0.775 (Beam Retrieval)
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question AnsweringarXiv:1809.09600
  • Question AnsweringonHotpotQA
    SUP-EM· 2018-09-25
    0.039
    best: 0.663 (Beam Retrieval)
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question AnsweringarXiv:1809.09600
  • Question AnsweringonHotpotQA
    SUP-F1· 2018-09-25
    0.377
    best: 0.901 (Beam Retrieval)
    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question AnsweringarXiv:1809.09600

Audio1 result

  • 2D Semantic SegmentationonxBD
    Weighted Average F1-score· 2019-11-21
    0.265
    best: 0.8141 (MambaBDA-Base)
    SOTA
    xBD: A Dataset for Assessing Building Damage from Satellite ImageryarXiv:1911.09296