Multi-View Attention Networks for Visual Dialog
Table 1: Results on VisDial v1.0 (test-std) and v0.9 (val). † denotes ensembles.
['Model', 'VisDial v1.0 (test-std) NDCG', 'VisDial v1.0 (test-std) MRR', 'VisDial v1.0 (test-std) R@1', 'VisDial v1.0 (test-std) R@5', 'VisDial v1.0 (test-std) R@10', 'VisDial v1.0 (test-std) Mean', 'VisDial v0.9 (val) MRR', 'VisDial v0.9 (val) R@1', 'VisDial v0.9 (val) R@5', 'VisDial v0.9 (val) R@10', 'VisDial v0.9 (val) Mean']
For VisDial v1.0, our MVAN model outperforms the previous state-of-the-art methods with respect to all evaluation metrics. Specifically, MVAN improves the performance from 57.59 to 59.37 for NDCG and 64.22 to 64.84 for MRR. In addition, we observe significant improvements in the Mean from 4.11 to 3.97, and an improvement in the R@k performance by approximately 0.4%. Similar results can be seen in the R@5 and Mean for VisDial v0.9. We also report the results for an ensemble of 10 independent models that were trained with different initial seeds, which yields average performance improvements of 1.3% for all metrics. These results indicate that our MVAN model not only has accurate prediction ability, as indicated by the non-NDCG metric results (i.e., MRR, R@k, and Mean), but it has a powerful generalization capability given the result of NDCG score because this metric considers several relevant answers to be correct.