DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
Table 1: Result comparison on validation set of VisDial v1.0.
['Model', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean', 'NDCG']
, re-trained by us). ReDAN-G and DMRM adopted complex multi-step reasoning, while HCIAE-G, CoAtt-G and Primary-G are attention-based models. For fairness, we only compare the original generative ability without re-ranking. We just replace the decoders in baseline models by our proposed DAM. Compared with the baseline models, our models outperform them on all the metrics, which indicates the complementary advantages between DAM and existing encoders in visual dialogue. Though DualVD-G performs lower than DMRM on Mean, DualVD-DAM outperforms DMRM on all the other metrics without multi-step reasoning, which is the advantages in DMRM over our models.