DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog
Table 2: Performance on VisDial val v1.0 [das2017visual]. ‡ All the models are re-implemented by gan2019multi gan2019multi.
['Model', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean']
We compare our proposed models to the state-of-the-art generative models developed in previous works. The key observations are as follows: By comparing with singe-hop approaches (LF [das2017visual] and HCAIE [ lu2017best]) , we demonstrate the validity of multi-hop reasoning, because it utilizes the abundant latent information between modalities. By comparing with singe-channel approaches (CoAtt [wu2018you] and RvA [niu2019recursive]), we come to the conclusion that dual-channel reasoning is beneficial for gaining an original understanding of the question from the dialog history and the image. By comparing with other methods and the state-of-the-art approaches (HREA [das2017visual], MN [das2017visual] and ReDAN [gan2019multi]) , our approach achieves the state-of-the-art results on some metrics that demonstrate the superiority of our model.