## 5 Experiments
### Experimental Results
The results demonstrate that the accuracy of our approaches greatly improved, approximately tripling from the baseline (maximum accuracy for the nine-sprite-added datasets increased from 24.74 to 75.84). In this case, one of the Google models outperformed the other models in terms of accuracy, which is different from the baseline result. Also, surprisingly, the lightweight model (ResNet-18) is comparable to the best Vision Transformer-based model in terms of accuracy. This may be interpreted as the Vision Transformer models we use not exploiting large enough datasets for their training. The results also show that the elapsed times are significantly different from the baseline approach. Although the elapsed time for embedding the 69 sprites does not change substantially, the elapsed time for embedding detected objects of backdrops and the elapsed time for finding the k-nearest neighbors are significantly higher than the baseline. This means that there are many detected objects in the backdrops when using YOLOS-Ti, therefore, we must spend a lot of time embedding objects and finding the nearest neighbors for each embedded object. Obviously, most of the detected objects were false positives. However, by performing the process of finding the k-nearest neighbors, most of the false positives were removed, allowing us to achieve high accuracy. Also, even with the many false positives occurring in these results, when using a lightweight CNN-based embedding model, the elapsed time is affordable even when using a laptop computer. Especially if \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Our Model (with YOLOS-Ti)** & **\# Objects** & **E. Sprites** & **E. Backdrops** & **Finding kNN** & **Accuracy (\%)** \\  & **(sec)** & **(sec)** & **(sec)** & **(sec)** & **(sec)** \\ \hline \hline \multirow{3}{*}{ResNet-18} & 3 & 0.00435 & 0.21694 & 0.25879 & 75.27 \\  & 6 & 0.00383 & 0.23732 & 0.28784 & 74.12 \\  & 9 & 0.00357 & 0.27198 & 0.32854 & 73.84 \\ \hline \multirow{3}{*}{VGG-16} & 3 & 0.00715 & 0.26346 & 0.26026 & 70.27 \\  & 6 & 0.00975 & 0.28967 & 0.29331 & 69.83 \\  & 9 & 0.00703 & 0.33154 & 0.32995 & 70.40 \\ \hline \multirow{3}{*}{clip-ViT-B-32} & 3 & 0.02960 & 0.84996 & 0.26091 & 59.10 \\  & 6 & 0.02942 & 0.93125 & 0.28929 & 61.43 \\  & 9 & 0.02990 & 1.10837 & 0.32836 & 64.10 \\ \hline \multirow{3}{*}{google/vit-base-patch16-224-in21k} & 3 & 0.09293 & 6.62304 & 0.36970 & **75.43** \\  & 6 & 0.10465 & 7.58749 & 0.40287 & **75.03** \\  & 9 & 0.09675 & 8.46929 & 0.45842 & **75.84** \\ \hline \multirow{3}{*}{google/vit-base-patch32-224-in21k} & 3 & 0.04208 & 2.57006 & 0.36277 & 70.30 \\  & 6 & 0.03495 & 2.86321 & 0.41135 & 71.88 \\  & 9 & 0.04234 & 3.28558 & 0.46758 & 72.87 \\ \hline \multirow{3}{*}{facebook/dino-vit8} & 3 & 0.14997 & 9.52359 & **0.19336** & 69.80 \\  & 6 & 0.14184 & 10.62992 & **0.21481** & 64.65 \\ \cline{1-1}  & 9 & 0.14475 & 12.46685 & **0.25093** & 64.38 \\ \hline \multirow{3}{*}{facebook/dino-vitb16} & 3 & 0.10165 & 7.12740 & 0.36584 & 71.17 \\ \cline{1-1}  & 6 & 0.10426 & 7.87116 & 0.41065 & 66.82 \\ \cline{1-1}  & 9 & 0.10952 & 8.45760 & 0.45791 & 66.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of our approaches that utilize both the supervised object detection model named YOLOS-Ti and embedding models based on CNNs or Vision Transformers. we used the approximate k-nearest-neighbor search algorithm, the elapsed time could be much shortened. Table 4 illustrates the results of our approach when utilizing the unsupervised object detection model, CutLER. The findings reveal that our approach with the unsupervised object detection model consistently outperformed the supervised object detection model across all tested cases in terms of accuracy. For instance, within a dataset of three sprites, the baseline accuracy was 9.07, while our approach's accuracy reached 75.27 with YOLOS-Ti and an even higher value, 80.57, with CutLER.