HyperAI

Visual Question Answering On Gqa Test2019

Metriken

Accuracy
Binary
Consistency
Distribution
Open
Plausibility
Validity

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
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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