History for Visual Dialog: Do we really need it?
Table 3: Automatic evaluation on the subsets of VisPro and VisDialConv dataset. We found history based MCA models to outperform significantly compared to the MCA-I model. On VisDialConv, MCA-I-VGH still outperform all other models in spare annotation phase while MCA-I-HGuidedQ performs the best after fine-tuning.
['[BOLD] Model', '[BOLD] Sparse annotation Phase  [BOLD] NDCG ↑', '[BOLD] Sparse annotation Phase  [BOLD] MRR ↑', '[BOLD] Sparse annotation Phase  [BOLD] R@1 ↑', '[BOLD] Sparse annotation Phase  [BOLD] R@5 ↑', '[BOLD] Sparse annotation Phase  [BOLD] R@10 ↑', '[BOLD] Sparse annotation Phase  [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 ↓']
They show that history encoding models (MCA-I-HGuidedQ / MCA-I-HConcQ / MCA-I-H / MCA-I-VGH) significantly outperform MCA-I, suggesting that this data cannot be modelled as multiple rounds of VQA. It can also be seen that all the models with dense (early) interaction of the historical context outperform the one with late interaction (MCA-I-H) in terms of NDCG. Models with dense interactions appear to be more reliable in choosing other correct relevant answers because of the dialog context.