Image-Question-Answer Synergistic Network for Visual Dialog
Table 1: Performance of discriminative models on test-standard server of Visual Dialog Challenge 2018. We ensemble 10 models with different seeds and varying M from 25 to 35.
['[BOLD] Model', '[BOLD] NDCG', '[BOLD] MRR', '[BOLD] R@1', '[BOLD] R@5', '[BOLD] R@10', '[BOLD] Mean']
In the primary stage, some answers are improperly scored due to the limitations of the scoring method. In this synergistic stage, answers are coordinated with the question and image for re-rank. However, easy candidate answers are not needed in further analysis, and we want our second stage model to fully focus on hard answers within its modeling capacity. Therefore, we select the answers with higher probability of being correct based on the predicted scores from the primary stage. which means that the remaining 90 answers are of lower probability and can easily be discriminated. Based on this phenomenon, we first pick the top N answers from At to organize a new candidate set Bt, where Bt=(bt,1,…,bt,N),Bt⊂At. To improve accuracy, we ensemble 10 models with different seeds and M. We rank the answers by summing scores of all models and achieve the highest NDCG on the test-standard server of Visual Dialog Challenge 2018.