Granular Multimodal Attention Networks for Visual Dialog
Table 1: Ablation analysis of our model on VisDial-v1.0 in test-std dataset.
['[BOLD] Model', '[BOLD] R@1', '[BOLD] R@5', '[BOLD] R@10', '[BOLD] MRR', '[BOLD] Mean', '[BOLD] NDGC']
Each row provides results for one of the variations. The first block provides scores of our implementation for traditional methods such as stack attention method which is our baseline, History-Conditioned Image Attentive Encoder (HCIAE) based attention method. The second block provides scores for our proposed Granular Image (GIA) and Text(GTA) attention model and the third block provides scores for different variant of our proposed multimodal attention model GMA. It is apparent that our best variant (GMA(MCB-att)) outperforms all the other variants achieving an improvement of 3.57% in R@1 score, 4.22% in R@5 score, 3.77% in R@10 score, 0.0899% in MRR and 2.27% in mean over the baseline variant.