DRAU: Dual Recurrent Attention Units for Visual Question Answering
Table 3: DRAU compared to the state-of-the-art on the VQA 1.0 dataset. N corresponds to the number of models used for prediction. WE indicates whether the method uses a pre-trained word embedding. VG indicates whether the method uses external data from the Visual Genome dataset.
['VQA 1.0 Open Ended Task', 'VQA 1.0 Open Ended Task', 'VQA 1.0 Open Ended Task', 'VQA 1.0 Open Ended Task', 'VQA 1.0 Open Ended Task Test-dev', 'VQA 1.0 Open Ended Task Test-dev', 'VQA 1.0 Open Ended Task Test-dev', 'VQA 1.0 Open Ended Task Test-dev', 'VQA 1.0 Open Ended Task Test-standard', 'VQA 1.0 Open Ended Task Test-standard', 'VQA 1.0 Open Ended Task Test-standard', 'VQA 1.0 Open Ended Task Test-standard']
S4SS3SSS0Px1 Vqa 1.0 Excluding model ensembles, DRAU performs favorably against other models. To the best of our knowledge, Yu et al. Our single model (DRAU) comes a very close second to the current state-of-the-art single model.