# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Experiments on Imagenet Categories
#### 4.2.3 Results  
The experimental evaluation is performed under two ZSL settings: classic and generalized. Only unseen class labels and both unseen and seen class labels are considered as target categories for prediction in the classic setting and the generalized setting, respectively. Note that the ground-truths of the test image instances represent only unseen class labels. The performance of each model is evaluated in terms of the average per-class accuracy, _\"per-class\"_ and an overall accuracy for all instances, _\"per-instance\"_. Note that the accuracy values are expressed in percentage in all our experiments. We apply the proposed contextual calibrations to the visual inferences of four baseline models for validation, giving seven types of evaluation per baseline model--three SMs (_path_, _lcl_, and _wup_), CS, and HMs--for each of the three semantic similarities. * Comparative Evaluation to Baselines  
Table 2 shows the results of our comparison with the baselines. All the prediction models using any type of the contextual inferences significantly outperform the baseline models in both settings. In most cases, the contextual inferences with harmonic measures are more effective than the others. SJE, LatEm, ConSE, and GCN baselines result in _per-class_ accuracies of 11.64, 11.99, 11.72, and 13.37 in the classic setting, respectively, and _per-class_ accuracies of 1.7, 0.78, 0.43, and 2.96 in the generalized setting, respectively. Note that the bold values in all tables represent the maximum accuracies. Compared to those performances, when applied to the baselines, SJE, LatEm, ConSE, and GCN, our model deduces _per-class_ maximum accuracies (bold values) of 19.49 (increase rate of 67%), 18.27 (52%), \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{4}{c}{**Classic**} & \multicolumn{2}{c}{**Generalized**} \\  & & & **Per-Class** & **Per-Instance** & **Per-Class** & **Per-Instance** \\ \hline SJE (**baseline**) & & & 11.64 & 13.2 & 1.7 & 2.03 \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{SM} & path & 15.86 & 16.93 & 3.2 & 4.43 \\  & & lch & 15.54 & 16.97 & 3.3 & 4.51 \\  & & wup & 15.39 & 16.67 & 3.31 & 4.56 \\ \cline{2-6}  & \multirow{2}{*}{CS} & & 17.1 & 17.52 & 1.7 & 2.06 \\  & & path & 18.56 & 18.77 & 3.2 & 4.43 \\  & & lch & **19.49** & **19.68** & 3.3 & 4.51 \\  & & wup & 19.25 & 19.2 & **3.31** & **4.56** \\ \hline LatEm (**baseline**) & & & 11.99 & 13.22 & 0.78 & 0.83 \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{SM} & path & 15.7 & 16.25 & 3.2 & 4.43 \\  & & lch & 15.69 & 16.46 & 3.3 & 4.51 \\  & & wup & 15.92 & 16.7 & 3.31 & 4.56 \\ \cline{2-6}  & \multirow{2}{*}{CS} & & 16.83 & 16.95 & 1.43 & 2.06 \\  & & path & 17.36 & 17.65 & 3.2 & 4.43 \\  & & lch & 17.85 & 18.24 & 3.3 & 4.51 \\  & & wup & **18.27** & **18.37** & **3.31** & **4.56** \\ \hline ConSE (**baseline**) & & & 11.72 & 12.03 & 0.43 & 0.38 \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{SM} & path & 15.15 & 14.85 & 3.2 & 4.43 \\  & & lch & 15.26 & 15.51 & 3.3 & 4.51 \\  & & wup & 15.07 & 15.11 & 3.31 & 4.56 \\ \cline{2-6}  & \multirow{2}{*}{CS} & & 14.49 & 14.34 & 1.43 & 2.06 \\  & & path & 16.09 & 15.59 & 3.2 & 4.43 \\  & & lch & **16.36** & **16.91** & 3.3 & 4.51 \\  & & wup & 16.35 & 16.29 & **3.31** & **4.56** \\ \hline GCN (**baseline**) & & & 13.37 & 11.8 & 2.96 & 2.37 \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{SM} & path & 16.34 & 15.15 & 4.54 & 5.11 \\  & & lch & 16.39 & 15.13 & 4.34 & 5.4 \\ \cline{1-1}  & & wup & 15.93 & 14.28 & 3.99 & 5.3 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{CS} & & 18.44 & 16.91 & 2.96 & 2.37 \\ \cline{1-1}  & & path & 19.77 & 17.69 & **4.54** & 5.15 \\ \cline{1-1}  & & lch & 20.43 & **18.52** & 4.34 & **5.4** \\ \cline{1-1}  & & wup & **20.55** & 17.92 & 3.99 & 5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative results to the baseline models. Numerical values represent top-1 accuracy in percentage.