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ResearchFormerly WizWand

Zero-shot recognition on Animals with Attributes (AwA)

76.33AccuracySSE-ReLU
39.066848.740958.41568.0891Sep 15, 2015Jan 28, 2016Jun 11, 2016Oct 24, 2016Mar 8, 2017Jul 21, 2017Dec 4, 2017
Updated 8mo ago

Evaluation results

MethodLinks
SSE-ReLUFeature=vgg-verydeep-192015.0976.33
Romera-Paredes and Torr [30]Feature=vgg-verydeep-192015.0975.32
SSE-INTFeature=vgg-verydeep-192015.0971.52
LATEMFeature Generator=f-CLSWGAN2017.1269.9
ALEFeature Generator=f-CLSWGAN2017.1268.2
SoftmaxFeature Generator=f-CLSWGAN2017.1268.2
DEVISEFeature Generator=f-CLSWGAN2017.1266.9
SJEFeature Generator=f-CLSWGAN2017.1266.9
SJEFeature Generator=none2017.1265.6
ESZSLFeature Generator=f-CLSWGAN2017.1263.9
Akata et al. [2]Feature=AlexNet2015.0961.9
ALEFeature Generator=none2017.1259.9
ESZSLFeature Generator=none2017.1258.2
Lampert et al. [16]Feature=vgg-verydeep-192015.0957.23
LATEMFeature Generator=none2017.1255.1
DEVISEFeature Generator=none2017.1254.2
Romera-Paredes and Torr [30]Feature=Non-CNN2015.0949.3
Yu et al. [42]Feature=Non-CNN2015.0948.3
Fu et al. [10]Feature=Non-CNN2015.0947.1
Akata et al. [1]Feature=Non-CNN2015.0943.5
Jayaraman and Grauman [14]Feature=Non-CNN2015.0943.01
Wang and Ji [39]Feature=Non-CNN2015.0942.78
Rohrbach et al. [28]Feature=Non-CNN2015.0942.7
Lampert et al. [16]Feature=Non-CNN2015.0940.5