## 4 Experiments
### Ablation Studies  
To validate the design choices of our CriSALAD model, we conducted a series of ablation studies on the Nordland dataset [46]. Learning rate and scheduler: We experimented with two different learning rate and scheduler configurations. Specifically, we compared performance using a learning rate of 6 \(\times\) 10\({}^{-5}\) with a linear scheduler, as adopted in SALAD [23], against a learning rate of 0.0001 with a step scheduler, as used in CricaVPR [19]. The results, presented in the first three rows of Table 2, indicate a significant degradation in place recognition performance when transitioning from 6 \(\times\) 10\({}^{-5}\) (linear scheduler) to 0.0001 (linear scheduler) and 6 \(\times\) 10\({}^{-5}\) (step scheduler). In particular, the average Top-1 recall rate dropped from 89.3% to 80.9% and 85.0%, respectively. These findings highlight the importance of selecting an appropriate learning rate and scheduler in optimizing model performance for visual place recognition. Depthwise Separable Convolutional Adapter: To assess the effectiveness of our adapter, we performed an ablation study by replacing the depthwise separable convolutional adapter with the multi-resolution convolutional adapter from CricaVPR [19] and a simple MLP adapter. All adapters were placed in the same position within the DiNOv2 backbone to ensure a fair comparison. The results, shown in the fourth and fifth rows of Table 2, indicate a notable drop in performance. The average Top-1 recall rate dropped from 89.3% to 84.8% when using the multi-resolution convolutional adapter and to 86.4% when using the MLP adapter. These findings suggest that the depthwise separable convolutional adapter achieves superior results and improves computational efficiency. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dim.**} & \multicolumn{3}{c}{**Pitts30k**} & \multicolumn{3}{c}{**Tokyo24/7**} & \multicolumn{3}{c}{**Nordland**} & \multicolumn{3}{c}{**MSLS-Val**} \\ \cline{3-13}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline NetVLAD [20] & 32,768 & 81.9 & 91.2 & 93.7 & 60.6 & 68.9 & 74.6 & - & - & - & 53.1 & 66.5 & 71.1 \\ Conv-AP [43] & 4096 & 89.4 & 94.7 & 95.9 & 81.0 & 88.3 & 92.4 & - & - & - & 69.2 & 80.3 & 83.1 \\ Patch-NetVLAD [24] & / & 88.7 & 94.5 & 95.9 & 86.0 & 88.6 & 90.5 & - & - & - & 79.5 & 86.2 & 87.7 \\ TransVPR [49] & / & 89.0 & 94.9 & 96.2 & 79.0 & 82.2 & 85.1 & - & - & - & 86.8 & 91.2 & 92.4 \\ CosPlace [31] & 512 & 88.4 & 94.5 & 95.7 & 81.9 & 90.2 & 92.7 & 58.5 & 73.7 & 79.4 & 82.8 & 89.7 & 92.0 \\ MixVPR [21] & 4096 & 91.5 & 95.5 & 96.3 & 85.1 & 91.7 & 94.3 & 76.2 & 86.9 & 90.3 & 88.0 & 92.7 & 94.6 \\ EigenPlaces [9] & 2048 & 92.5 & 96.8 & 97.6 & **93.0** & 96.2 & 97.5 & 71.2 & 83.8 & 88.1 & 89.1 & 93.8 & 95.0 \\ CricaVPR(16 bs) [19] & 4096 & 93.7 & 97.0 & **98.1** & **93.0** & **97.1** & **97.8** & - & - & - & 89.9 & 95.4 & 96.2 \\ DiNO-SALAD [23] & 8448 & 91.9 & 96.3 & 97.4 & 92.1 & 96.2 & 96.5 & 76.0 & 89.2 & 92.0 & **92.2** & **96.4** & **97.0** \\ \hline CriSALAD (ours) & 8448 & **94.7** & **97.2** & 97.8 & 90.8 & 95.6 & 97.1 & **89.3** & **95.7** & **97.2** & 87.4 & 93.8 & 95.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with state-of-the-art methods on benchmark datasets. The best results are indicated in **bold**, and the second-best results are underlined.