Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets
Table 6: Using models trained on qaVG to improve Visual7W and VQA (Accuracy in %).
['Datasets', 'Decoys', 'Best w/o using qaVG', 'qaVG model initial', 'qaVG model fine-tuned']
With qaVG, we also investigate whether it can help improve the multiple-choice performances on the other two datasets. We use the MLP-IQA trained on qaVG with both IoU and QoU decoys to initialize the models for the Visual7W and VQA datasets. We report the accuracies before and after fine-tuning, together with the best results learned solely on those two datasets. Fukui et al.