# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Experiments on Visual Genome Categories
#### 4.3.3 Results  
* Evaluation Result  
This experiment is conducted by matching its experimental configuration to Context-Aware (CA) ZSR [45] to the hilt, but the performance of the ConSE baseline is reproduced slightly differently due to a minute difference in the datasets, fine-tuning of the visual classifier, and the word embedding space.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Classic/U**} & \multicolumn{3}{c}{**Generalized/U**} \\ \cline{2-9}  & **hit@1** & \multicolumn{3}{c}{**hit@5**} & \multicolumn{3}{c}{**hit@1**} & \multicolumn{3}{c}{**hit@5**} \\  & **per-cls** & **per-ins** & **per-cls** & **per-ins** & **per-cls** & **per-ins** & **per-cls** & **per-ins** \\ \hline SJE & 11.64 & 13.2 & 35.36 & 37.56 & 1.7 & 2.03 & 6.14 & 7.39 \\ SJE + sim & 17.32 & 17.75 & 46.17 & 45.42 & 2.98 & 4.17 & 7.94 & 10.09 \\ SJE + sim + dis & **18.56** & **18.77** & **47.06** & **46.04** & **3.2** & **4.43** & **8.06** & **10.28** \\ \hline LatEm & 11.99 & 13.22 & 32.85 & 34.32 & 0.78 & 0.83 & 4.72 & 5.28 \\ LatEm + sim & 16.46 & 17.2 & 39.67 & 39.09 & 2.4 & 3.67 & 6.05 & 7.75 \\ LatEm + sim + dis & **17.36** & **17.65** & **41.05** & **39.66** & **3.2** & **4.43** & **6.19** & **7.92** \\ \hline ConSE & 11.72 & 12.03 & 36.16 & 34.36 & 0.43 & 0.38 & 5.51 & 5.76 \\ ConSE + sim & 14.31 & 14.85 & 44.74 & 41.34 & 2.59 & 3.67 & 6.73 & 7.67 \\ CONSE + sim + dis & **16.09** & **15.59** & **45.27** & **42.23** & **3.2** & **4.43** & **6.84** & **7.78** \\ \hline GCN & 13.37 & 11.8 & 40.01 & 37.59 & 2.96 & 2.37 & 7.34 & 6.38 \\ GCN + sim & 19.34 & 17.06 & **47.57** & **44.94** & 4.12 & 4.64 & **10.23** & 9.77 \\ GCN + sim + dis & **19.77** & **17.69** & 47.15 & **44.94** & **4.54** & **5.15** & 10.11 & **9.85** \\ \hline \hline \end{tabular}
\end{table}
Table 4: An ablation study on HM\({}_{path}\).  
We thus evaluate performance in terms of the improvement rate using the results given in Table 5. Recall that the numerical values represent accuracy in percentage. In the top-1 of the classic setting, CA presents its performance as 19.6 (increase rate of \(-1.51\%\)) and 30.2 (9.02\(\%\)) for a baseline performance of 19.9 and 27.7 on per-class and per-instance, respectively, whereas our proposed method has a performance of 15.34 (2.33\(\%\)) and 32.62 (3.16\(\%\)), for 14.99 per-class and 31.62 per-instance. Compared with CA, our method shows enhanced per-class performance, but the per-instance influence is slightly deficient. In the top-1 in the generalized setting, our method produces relatively low enhancement (0.05-0.27 and 0.29-0.81, for per-class and per-instance, respectively) compared with that of the CA (0.1-5.8 and 0.6-20.7). In the top-5 evaluations, our absolute performance of 33.58 on per-instance in the generalized setting outperforms CA's 29.4, although the performance of the ConSE baseline is lower than that of CA.  
Consequently, the proposed method offers fair performance improvement in classifying an appropriate category compared with the existing method, and it more often gives categories related to the category type of the target object a high ranking, as shown by the first experimental results. This is furthermore validated through qualitative evaluations by actual exemplary analysis, which is detailed in the following subsection.