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Visual Question Answering On Gqa Test2019

المقاييس

Accuracy
Binary
Consistency
Distribution
Open
Plausibility
Validity

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
Accuracy
Binary
Consistency
Distribution
Open
Plausibility
Validity
Paper TitleRepository
fisher60.9877.3290.775.3646.5584.9396.38--
LININ60.5978.4492.667.2844.8385.3896.57--
LSTM+CNN46.5563.2674.577.4631.884.2596.02--
VinVL-DPT64.9282.6394.375.1149.2984.9196.64--
rishabh_test59.3777.5388.636.0643.3584.7196.18--
BgTest59.876.7489.145.1144.8584.296.23--
Future_Test_team60.1777.1989.615.8345.1484.4696.36--
UNITER + MAC + Graph Networks59.2977.3188.945.843.3884.4396.3--
Fj37.0356.6163.9628.419.7485.1295.76--
rsa-14word57.3575.0787.615.9441.7184.595.86--
VinVL+L64.8582.5994.04.5949.1984.9196.62VinVL+L: Enriching Visual Representation with Location Context in VQA
KU55.072.0983.475.2939.9284.6696.34--
BottomUp49.7466.6478.715.9834.8384.5796.18Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
graphRepresentation, Single57.8974.5485.455.7343.1984.9996.4--
GRN61.2278.6990.316.7745.8185.4396.36Bilinear Graph Networks for Visual Question Answering-
total1456.9574.6287.715.8141.3684.5795.98--
TESTOVQA00760.1876.9789.655.2945.3684.4796.33--
NSM single (updated)63.1778.9493.253.7149.2584.2896.41--
LW55.6572.8689.189.6940.4685.2796.33--
GM6_9_2_train56.9674.9785.127.1341.0684.8596.38--
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Visual Question Answering On Gqa Test2019 | SOTA | HyperAI