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Visual Question Answering on DAQUAR-ALL full (test)

50.2Accuracy

Human

Updated 1mo ago

Evaluation Results

Method	Links
Human 2015.11		50.2	50.8	67.3
Human Baseline 2016.03		50.2	50.82	62.27
SAN(2, LSTM) 2015.11		29.3	34.9	68.1
SAN(2, CNN) 2015.11		29.3	35.1	68.6
Yang et al. 2016.03		29.3	35.1	68.6
A+C+Selected-K-LSTM 2016.03		29.23	35.37	68.72
SAN(1, CNN) 2015.11		29.2	35.1	67.8
Att+Cap+Know-LSTM 2016.03		29.16	35.3	68.66
Noh et al. 2016.03		28.98	34.8	67.81
SAN(1, LSTM) 2015.11		28.9	34.7	68.5
Att+Cap-LSTM 2016.03		27.04	33.4	67.65
Att+Know-LSTM 2016.03		24.89	31.27	66.11
Att-LSTM 2016.03		24.27	30.41	62.29
Cap+Know-LSTM 2016.03		23.91	30.64	65.01
VggNet+ft-LSTM 2016.03		23.75	30.22	63.66
IMG-CNN 2015.11		23.4	29.6	63
Ma et al. 2016.03		23.4	29.59	62.95
VggNet-LSTM 2016.03		23.13	30.01	63.61
Language + IMG 2015.11		21.7	28	65
Askneuron 2016.03		19.43	25.28	62
Language 2015.11		19.1	25.2	65.1
Multi-World 2015.11		7.9	11.9	38.8