EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition

• URL: http://arxiv.org/abs/2405.18065v1
• Date: Tue, 28 May 2024 11:24:41 GMT
• Title: EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition
• Authors: 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,
• Abstract summary: We propose a simple yet powerful approach to better exploit the potential of a foundation model for Visual Place Recognition. We first demonstrate that features extracted from self-attention layers can serve as a powerful re-ranker for VPR. We then demonstrate that a single-stage method leveraging internal ViT layers for pooling can generate global features that achieve state-of-the-art results.
• Score: 6.996304653818122
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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