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Models/NIC (ResNet-50, CutMix)

NIC (ResNet-50, CutMix)

Reported on 7 benchmarks across 1 task · 1 paper · 7 SOTA

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

Natural Language Processing7 results

Image CaptioningonCOCO (Common Objects in Context)
BLEU-1· 2019-05-13
64.2
best: 81.1 (IGINet)
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899
Image CaptioningonCOCO (Common Objects in Context)
BLEU-2· 2019-05-13
46.3
best: 65.9 (IGINet)
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899
Image CaptioningonCOCO (Common Objects in Context)
BLEU-3· 2019-05-13
33.6
best: 51.7 (IGINet)
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899
Image CaptioningonCOCO (Common Objects in Context)
BLEU-4· 2019-05-13
24.9
best: 39.9 (IGINet)
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899
Image CaptioningonCOCO (Common Objects in Context)
CIDEr· 2019-05-13
77.6
best: 143.7 (ExpansionNet v2)
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899
Image CaptioningonCOCO (Common Objects in Context)
METEOR· 2019-05-13
23.1
best: 29.4 (IGINet)
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899
Image CaptioningonCOCO (Common Objects in Context)
ROUGE· 2019-05-13
49
SOTA
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features arXiv:1905.04899