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Models/human

human

Reported on 12 benchmarks across 2 tasks

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

Natural Language Processing7 results

  • Visual Question Answering (VQA)onGQA Test2019
    Accuracy
    89.3
  • Visual Question Answering (VQA)onGQA Test2019
    Binary
    91.2
  • Visual Question Answering (VQA)onGQA Test2019
    Consistency
    98.4
  • Visual Question Answering (VQA)onGQA Test2019
    Distribution
    0
    best: 93.08 (GlobalPrior)
  • Visual Question Answering (VQA)onGQA Test2019
    Open
    87.4
  • Visual Question Answering (VQA)onGQA Test2019
    Plausibility
    97.2
  • Visual Question Answering (VQA)onGQA Test2019
    Validity
    98.9

Robots5 results

  • Vision and Language NavigationonVLN Challenge
    error
    1.61
  • Vision and Language NavigationonVLN Challenge
    length
    11.85
    best: 1257.38 (Speaker-Follower)
  • Vision and Language NavigationonVLN Challenge
    oracle success
    0.9
    best: 1 (FOAM-Beam Search)
  • Vision and Language NavigationonVLN Challenge
    spl
    0.76
  • Vision and Language NavigationonVLN Challenge
    success
    0.86