EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition
<|reference_start|>EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition: The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on task-specific data. In this paper, we propose a simple yet powerful approach to better exploit the potential of a foundation model for VPR. We first demonstrate that features extracted from self-attention layers can serve as a powerful re-ranker for VPR. Utilizing these features in a zero-shot manner, our method surpasses previous zero-shot methods and achieves competitive results compared to supervised methods across multiple datasets. Subsequently, we demonstrate that a single-stage method leveraging internal ViT layers for pooling can generate global features that achieve state-of-the-art results, even when reduced to a dimensionality as low as 128D. Nevertheless, incorporating our local foundation features for re-ranking, expands this gap. Our approach further demonstrates remarkable robustness and generalization, achieving state-of-the-art results, with a significant gap, in challenging scenarios, involving occlusion, day-night variations, and seasonal changes.<|reference_end|>
@article{tzachor2024effovpr:,
    title={EffoVPR: Effective Foundation Model Utilization for Visual Place
  Recognition},
    author={Issar Tzachor, Boaz Lerner, Matan Levy, Michael Green, Tal Berkovitz
  Shalev, Gavriel Habib, Dvir Samuel, Noam Korngut Zailer, Or Shimshi, Nir
  Darshan, Rami Ben-Ari},
    journal={arXiv preprint arXiv:2405.18065},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18065},
    primaryClass={cs.CV cs.AI}
}