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Models/lxmert-adv-txt

lxmert-adv-txt

Reported on 7 benchmarks across 1 task

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

Natural Language Processing14 results

  • Visual Question Answering (VQA)onGQA Test2019
    Accuracy
    61.12
    best: 89.3 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Binary
    78.07
    best: 91.2 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Consistency
    91.13
    best: 98.4 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Distribution
    5.55
    best: 93.08 (GlobalPrior)
  • Visual Question Answering (VQA)onGQA Test2019
    Open
    46.16
    best: 87.4 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Plausibility
    84.8
    best: 97.2 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Validity
    96.36
    best: 98.9 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Accuracy
    61.1
    best: 89.3 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Binary
    77.99
    best: 91.2 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Consistency
    91.08
    best: 98.4 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Distribution
    5.52
    best: 93.08 (GlobalPrior)
  • Visual Question Answering (VQA)onGQA Test2019
    Open
    46.19
    best: 87.4 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Plausibility
    84.82
    best: 97.2 (human)
  • Visual Question Answering (VQA)onGQA Test2019
    Validity
    96.36
    best: 98.9 (human)