## 5 Experimental Results and Discussion
### Generalized Zero-Shot Cross-Modal Retrieval Results  
In the zero-shot learning experiment using CIFAR-10 dataset, we already showed how well the proposed model performs zero-shot cross-modal retrieval in Figure 6. However, to apply the proposed methodology in a more expandable manner, generalized zero-shot inference should be evaluated. The previous research has shown that the performance of zero-shot inference is significantly degraded in generalized case [7]. One way to improve the performance of generalized zero-shot learning is to determine if the input data is seen class or unseen class. In this experiment, we compare the performances of zero-shot model using HSL and zero-shot model using both HSL and UCE. In experiments, we use CIFAR-100 dataset as seen classes and CIFAR-10 dataset as unseen classes. The unseen semantic classes of CIFAR-10 used in the experiment are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. Table 2 shows that the proposed HSL+UCE model outperforms the basic HSL model in recognizing unseen class visual data. The HSL model uses HTL and HMSE in training the embedding model by using seen class data only. Naturally, the generalized zero-shot accuracy is low. It is difficult to retrieve the correct label for unseen class visual data among 110 classes consist of 100 seen-classes and 10 unseen-classes. In contrast, the HSL+UCE model has an additional UCE module for adjusting the distances between query visual vector and unseen class semantic vectors when the confidence of unseen class is high, and it improves the performance of generalized zero-shot cross-modal retrieval. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **hit@1** & **hit@2** & **hit@3** & **hit@4** & **hit@5** & **hit@6** & **hit@7** & **hit@8** & **hit@9** & **hit@10** \\ \hline HSL & & & & & & & & & & & \\ (100 & \multicolumn{1}{c}{Seen Classes \& 10 Unseen Classes Classes} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline HSL & & & & & & & & & & & \\ (100 & \multicolumn{1}{c}{Seen Classes \& 10 Unseen Classes Classes} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline HSL+UCE & & & & & & & & & & & \\ (100 & \multicolumn{1}{c}{Seen Classes \& 10 Unseen Classes Classes} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hit@k performance on generalized zero-shot cross-modal retrieval for unseen class visual data in CIFAR-10. Figure 6: Hit@1 zero-shot cross-modal retrieval performance for five zero-shot category sets in CIFAR-10. In the hit@1 of Table 2, the basic HSL shows a hit@1 performance of only 3.5%, while the HSL+UCE shows a performance of 13.8%. In hit@5, the basic HSL shows 19.9%, while the HSL+UCE shows 58% performance. The proposed methodology shows that the performance of generalized zero-shot cross-modal retrieval can be improved 3-4 times by applying UCE. Table 3 shows the actual images and results of the retrieved semantic labels used in this experiment. The first column in Table 3 shows the input query image belonging to the unseen class, the second column shows retrieved labels using the HSL model only, and the third column shows retrieved labels using the HSL+UCE model. The retrieved labels are arranged in order from top-1 to top-5, with predicted true labels highlighted in bold and underlined. Each row with a different image query shows three different cases of generalized zero-shot learning using the proposed methodology.