Joint Image Captioning and Question Answering
Table 1: Comparison of our results in VQA task with the state-of-the-art VQA methods on validation set and test-standard set. Accuracies in percentage (%) are reported.
['[EMPTY]', 'Test-standard Yes/No', 'Test-standard Num', 'Test-standard Other', 'Test-standard All']
We report the experimental results of the VQA task and compare our results with the state-of-the-art methods. As demonstrated in Table. In particular, we observe that our single model outperform other methods especially in ’Num’ and ’Other’ categories. The reasons are that the additional captions are capable of providing more numerical clues for infering ’Num’ type questions and more common knowledge for answering the ’Other’ type questions. Furthermore, an ensemble of 10 single models results in 69.7% in the test-standard split.