Graph-Structured Representations for Visual Question Answering
Table 2: Results on the test set of the “abstract scenes” dataset (average scores in percents).
['Method', 'Multiple choice Overall', 'Multiple choice Yes/no', 'Multiple choice Other', 'Multiple choice Number', 'Open-ended Overall', 'Open-ended Yes/no', 'Open-ended Other', 'Open-ended Number']
The evaluation is performed on an automated server that does not allow for an extensive ablative analysis. Anecdotally, performance on the validation set corroborates all findings presented above, in particular the strong benefit of pre-parsing, pretrained word embeddings, and graph processing with a GRU. At the time of our submission, our method occupies the top place on the leader board in both the open-ended and multiple choice settings. The advantage over existing method is most pronounced on the binary and the counting questions. Refer to Fig.