Image-Question-Answer Synergistic Network for Visual Dialog
Table 3: Performance of our generative model on validation dataset with candidate set.
['[BOLD] Model', '[BOLD] N', '[BOLD] M', '[BOLD] MRR', '[BOLD] R@1', '[BOLD] R@5', '[BOLD] R@10', '[BOLD] Mean']
It can be seen that the primary stage outperforms baseline by 1.1% with respect to MRR, which generates a strong candidate set for the next stage. Our synergistic stage further improves MRR by 2.6% when N=10, since the primary stage focuses on each word but lacks the understanding of whole answer sequence. In contrast to the discriminative model in which about 90% of correct answers are top ten ranked in the primary stage, only 2 out of 3 correct answers are top sorted by the generative model, so we increase the selected answer number N from 10 to 30 in the second stage, which further increases MRR by 2.1%. Furthermore, our model improves R@5 by 9.2% and R@10 by 8.9%.