DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
Table 2: Comparison on test-standard split of VisDial v1.0.
['Model', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean', 'NDCG']
Our model consistently outperforms all the approaches on most metrics, which highlights the importance of visual understanding from visual and semantic modules in visual dialogue. CoAtt and HeiCoAtt-QI are relevant to our model in the sense that they leverage attention mechanism to identify question-relevant visual features. However, they ignore the semantic-rich relationships and language priors. It should be noted that our model and the compared approaches all belong to single-step models. With the success of multi-step reasoning, ReDAN We believe that stacking our visual encoder to achieve multi-step visual understanding is a promising future work. DL-61 We believe that our model for the visual part and existing works for the dialogue or answer parts have complementary advantages.