History for Visual Dialog: Do we really need it?
Table 7: Hyper-parameter tuning for number of layers and number of heads. The results in the main paper are reported with 6 Layers(L6) and 8 Heads (H8) for all MCA models.
['[BOLD] Model', '[BOLD] Training  [BOLD] NDCG', '[BOLD] Training  [BOLD] MRR', '[BOLD] Training  [BOLD] R@1', '[BOLD] Training  [BOLD] R@5', '[BOLD] Training  [BOLD] R@10', '[BOLD] Training  [BOLD] Mean', '[BOLD] Curriculum Fine-tuning  [BOLD] NDCG', '[BOLD] Curriculum Fine-tuning  [BOLD] MRR', '[BOLD] Curriculum Fine-tuning  [BOLD] R@1', '[BOLD] Curriculum Fine-tuning  [BOLD] R@5', '[BOLD] Curriculum Fine-tuning  [BOLD] R@10', '[BOLD] Curriculum Fine-tuning  [BOLD] Mean']
For the results on the validation set, only the training split is used. To report results on test-std set, both the training and val set are used for training. For curriculum fine-tuning we use multi-class cross entropy loss where weighted by the relevance score. All our MCA modules have 6 layers and 8 heads, which we determined via a hyper parameter search.