Reasoning Visual Dialogs with Structural and Partial Observations
Table 3: Quantitative evaluation on VisDial-Q dataset [9, 22] with VisDial-Q evaluation protocol. See subsection 4.3 for more details.
['Methods', 'MRR ↑', 'R@1 ↑', 'R@5 ↑', 'R@10 ↑', 'Mean ↓']
Quantitative Results: The ablative models include i) our model with constant graph (all edge weights are 1), and ii) our model without the EM iterations. Our full model with 3 EM iterations outperforms the comparative method in all evaluation metrics. Particularly, we can see that our model with constant graph has a similar performance to the comparative method. This shows the effectiveness of our EM-based inference process. Experiment results on this dataset also shows the generality of our approach: it can infer the underlying dialog structure and reason accordingly about unobserved nodes (next question or current answer).