Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets
Table 11: Test accuracy (%) on Visual7W, comparing different embeddings for questions and answers. The results are reported for the IoU +QoU-decoys.
['Method', 'GloVe', 'Translation', 'word2vec']
We consider GloVe Pennington et al. and the embedding learned from translation McCann et al. We do not observe significant difference among different embeddings, which is likely due to that both the questions and answers are short (averagely 7 words for questions and 2 for answers).