# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 5 Experimental Results and Analysis
### Comparison with State-of-the-Art Methods
#### 5.1.2 Results on VisPro and VisDialConv Datasets  
To examine our model's performance even further, we report the performance results of our model on those of VisPro and VisDialConv datasets [9], in which humans must browse the dialog history to predict the answers for the current question. We contrasted our model's outcomes with those of MCA-I-HGuidedQ, MCA-I-VGH, and MCA-I-H from the work of [PERSON] et al. [9], who incorporated the dialog history in various ways using VisPro and VisDialConv datasets. Table 4 shows that our model continues to yield much better results over all of the metrics and that it has powerful generalization and prediction abilities owing to the collaborative intersection of semantic information with different granularity. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **NDCG\(\uparrow\)** & **MRR\(\uparrow\)** & **R@1\(\uparrow\)** & **R@5\(\uparrow\)** & **R@10\(\uparrow\)** & **Mean\(\downarrow\)** \\ \hline
**LF** & 45.31 & 55.42 & 40.95 & 72.45 & 82.83 & 5.95 \\
**HRE** & 45.46 & 54.16 & 39.93 & 70.45 & 81.50 & 6.41 \\
**MN** & 47.50 & 55.49 & 40.98 & 72.30 & 83.30 & 5.92 \\
**CorefNMN** & 54.70 & 61.50 & 47.55 & 78.10 & 88.80 & 4.40 \\
**GNN** & 52.82 & 61.37 & 47.33 & 77.98 & 87.83 & 4.57 \\
**FGA** & 52.10 & 63.70 & 49.58 & 80.97 & 88.55 & 4.51 \\
**DVAN** & 54.70 & 62.58 & 48.90 & 79.35 & 89.03 & 4.36 \\
**RVA** & 55.59 & 63.03 & 49.03 & 80.40 & 89.83 & 4.18 \\
**DualVD** & 56.32 & 63.23 & 49.25 & 80.23 & 89.70 & 4.11 \\
**HACAN** & 57.17 & 64.22 & 50.88 & 80.63 & 89.45 & 4.20 \\
**Synergistic** & 57.32 & 62.20 & 47.90 & 80.43 & 89.95 & 4.17 \\
**DAN** & 57.59 & 63.20 & 49.63 & 79.75 & 89.35 & 4.30 \\
**RAA-Net** & 55.42 & 62.86 & 49.05 & 79.65 & 88.85 & 4.35 \\
**HESNet** & 57.13 & 63.21 & 49.70 & 80.63 & 89.65 & 4.19 \\
**CAG** & 56.64 & 63.49 & 49.85 & 80.63 & 90.15 & 4.11 \\
**LTMI-LG** & 58.55 & 64.00 & 50.63 & 80.58 & 90.20 & 4.12 \\
**VD-BERT** & 59.96 & **65.44** & 51.63 & **82.23** & 90.68 & 3.90 \\
**MVAN** & 59.37 & 64.84 & 51.45 & 81.82 & 90.65 & 3.97 \\ \hline
**MGSCRN** & **60.98** & 65.40 & **51.92** & 81.94 & **90.89** & **3.88** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison on the test-standard dataset of VisDial v1.0 under the discriminative decoder setting. Better performance is indicated by “\(\uparrow\)” when the value is higher and by “\(\downarrow\)” when the value is lower. The best results are shown in bold. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **NDCG\(\uparrow\)** & **MRR\(\uparrow\)** & **R@1\(\uparrow\)** & **R@5\(\uparrow\)** & **R@10\(\uparrow\)** & **Mean\(\downarrow\)** \\ \hline
**Synergistic** & 57.88 & 63.42 & 49.30 & 80.77 & 90.68 & 3.97 \\
**DAN** & 59.36 & 64.40 & 59.90 & 81.18 & 90.40 & 3.99 \\
**CDF** & 59.49 & 64.40 & 50.90 & 81.18 & 90.40 & 3.99 \\
**MVAN** & 60.92 & 66.38 & 53.20 & 82.45 & 91.85 & 3.68 \\ \hline
**MGSCRN** & **62.25** & **67.42** & **53.99** & **82.96** & **92.14** & **3.60** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different methods of performing ensemble models on the test-standard dataset of VisDial v1.0. Better performance is indicated by “\(\downarrow\)” when the value is higher and by “\(\downarrow\)” when the value is lower. The best results are shown in bold. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **NDCG\(\uparrow\)** & **MRR\(\uparrow\)** & **R@1\(\uparrow\)** & **R@5\(\uparrow\)** & **R@10\(\uparrow\)** & **Mean\(\downarrow\)** \\ \hline
**LTMI** & 60.92 & 60.65 & 47.00 & 77.03 & 87.75 & 4.90 \\
**ReDAN** & 61.86 & 53.13 & 41.38 & 66.07 & 74.50 & 8.91 \\
**MVAN** & 63.15 & 63.02 & 49.43 & 79.48 & 89.40 & 4.38 \\ \hline
**MGSCRN** & **64.13** & **63.87** & **50.28** & **80.26** & **90.02** & **4.11** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different methods of performing multitask learning on the test-standard dataset of VisDial v1.0.