## 5 Conclusions  
In this paper, we introduced CriSALAD, a novel VPR model that fully leverages the generalization capabilities of extensively pre-trained visual foundation models, allowing it to extract environment-invariant features. To improve task-specific adaptability, we incorporated a depthwise separable convolutional adapter into the pre-trained backbone, enabling efficient fine-tuning by updating only the adapter's weights. Furthermore, we integrated the SALAD descriptor to both optimize global feature aggregation and suppress dynamic interference and leveraged cross-image correlations to strengthen contextual representations, thereby facilitating more robust scene understanding. Our CriSALAD model achieved promising performance on several datasets. Particularly, on the Nordland dataset, CriSALAD achieved a Recall@1 of 89.3%, surpassing the closest competitor by 13.1 percentage points (76.2%). In future work, we aim to enhance the model's generalization ability to effectively address challenges such as illumination variations and occlusions in visual place recognition tasks. Additionally, we plan to expand our experimental framework to include a broader range of unstructured environments, such as indoor settings and underwater scenarios. Conceptualization, J.X., Y.M., M.X., Y.F., Y.Z. and W.K. ; methodology, J.X., Y.M. and M.X. ; software, J.X., Y.M., Y.F., Y.Z. and M.X. ; writing--original draft preparation, J.X., Y.M., Y.F., Y.Z., M.X. and W.K. ; writing--review and editing, J.X., Y.M., Y.F., Y.Z., M.X. and W.K. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the Zhejiang Provincial Natural Science Foundation of China under grant No. LQN25F030015. **Institutional Review Board Statement:** Not applicable. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **MACs (M)** & **FLOPs (M)** & **Params (K)** \\ \hline MLP adapter & 1.181 & 2.362 & 2356.08 \\ MultiConv adapter & 44.236 & 88.472 & 761.472 \\ Our adapter & 39.223 & 78.446 & 741.504 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of adapter efficiency: FLOPs, MACs, and parameter counts, all calculated on a per-sample basis for the forward pass during inference. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** This research utilizes publicly available data for training and evaluation. The GSV-Cities dataset is available at [[https://www.kaggle.com/datasets/amaralibey/gsv-cities](https://www.kaggle.com/datasets/amaralibey/gsv-cities)]([https://www.kaggle.com/datasets/amaralibey/gsv-cities](https://www.kaggle.com/datasets/amaralibey/gsv-cities)) (accessed on 4 January 2025), the Pitts30k dataset is available at [[https://github.com/Relja/netvlad/issues/37](https://github.com/Relja/netvlad/issues/37)]([https://github.com/Relja/netvlad/issues/37](https://github.com/Relja/netvlad/issues/37)) (accessed on 4 January 2025), the Tokyo247 dataset is available at [[http://www.ok.ctrl.titech.ac.jp/~torii/project/247/](http://www.ok.ctrl.titech.ac.jp/~torii/project/247/)]([http://www.ok.ctrl.titech.ac.jp/~torii/project/247/](http://www.ok.ctrl.titech.ac.jp/~torii/project/247/)) (accessed on 4 January 2025), the Nordland dataset is available at [[https://github.com/jmfacil/single-view-place-recognition](https://github.com/jmfacil/single-view-place-recognition)]([https://github.com/jmfacil/single-view-place-recognition](https://github.com/jmfacil/single-view-place-recognition)) (accessed on 4 January 2025), and the MSLS dataset can be found at [[https://www.mapillary.com/dataset/places](https://www.mapillary.com/dataset/places)]([https://www.mapillary.com/dataset/places](https://www.mapillary.com/dataset/places)) (accessed on 4 January 2025).