Utilizing visual place recognition (VPR) technology to ascertain the
geographical location of publicly available images is a pressing issue for
real-world VPR applications. Although most current VPR methods achieve
favorable results under ideal conditions, their performance in complex
environments, characterized by lighting variations, seasonal changes, and
occlusions caused by moving objects, is generally unsatisfactory. In this
study, we utilize the DINOv2 model as the backbone network for trimming and
fine-tuning to extract robust image features. We propose a novel VPR
architecture called DINO-Mix, which combines a foundational vision model with
feature aggregation. This architecture relies on the powerful image feature
extraction capabilities of foundational vision models. We employ an
MLP-Mixer-based mix module to aggregate image features, resulting in globally
robust and generalizable descriptors that enable high-precision VPR. We
experimentally demonstrate that the proposed DINO-Mix architecture
significantly outperforms current state-of-the-art (SOTA) methods. In test sets
having lighting variations, seasonal changes, and occlusions (Tokyo24/7,
Nordland, SF-XL-Testv1), our proposed DINO-Mix architecture achieved Top-1
accuracy rates of 91.75%, 80.18%, and 82%, respectively. Compared with SOTA
methods, our architecture exhibited an average accuracy improvement of 5.14%.

通过利用 DINOv2 模型作为骨干网络对图像进行修剪和微调以提取鲁棒的图像特征，我们提出了一种名为 DINO-Mix 的新颖的 VPR 架构，通过基础视觉模型的强大图像特征提取能力和基于 MLP-Mixer 的特征聚合模块，实现全局鲁棒和可泛化的特征描述，从而实现高精度的 VPR。我们的实验证明，所提出的 DINO-Mix 架构显著优于当前最先进的方法，在具有光照变化、季节变化和遮挡的测试集上，分别达到了 91.75%、80.18% 和 82% 的 Top-1 准确率。与最先进的方法相比，我们的架构平均准确率提高了 5.14%。