Visual Geo-localization (VG) refers to the process to identify the location described in query images, which is widely applied in robotics field and computer vision tasks, such as autonomous driving, metaverse, augmented reality, and SLAM. In fine-grained images lacking specific text descriptions, directly applying pure visual methods to represent neighborhood features often leads to the model focusing on overly fine-grained features, unable to fully mine the semantic information in the images. Therefore, we propose a two-stage training method to enhance visual performance and use contrastive learning to mine challenging samples. We first leverage the multi-modal description capability of CLIP (Contrastive Language-Image Pretraining) to create a set of learnable text prompts for each geographic image feature to form vague descriptions. Then, by utilizing dynamic text prompts to assist the training of the image encoder, we enable the image encoder to learn better and more generalizable visual features. This strategy of applying text to purely visual tasks addresses the challenge of using multi-modal models for geographic images, which often suffer from a lack of precise descriptions, making them difficult to utilize widely. We validate the effectiveness of the proposed strategy on several large-scale visual geo-localization datasets, and our method achieves competitive results on multiple visual geo-localization datasets. Our code and model are available at https://github.com/Chain-Mao/ProGEO.

翻译：视觉地理定位（VG）指识别查询图像中所描述位置的过程，广泛应用于机器人领域和计算机视觉任务，如自动驾驶、元宇宙、增强现实和SLAM。在缺乏特定文本描述的细粒度图像中，直接应用纯视觉方法表示邻域特征常导致模型关注过于细粒度的特征，无法充分挖掘图像中的语义信息。为此，我们提出一种两阶段训练方法以增强视觉性能，并利用对比学习挖掘困难样本。我们首先利用CLIP（对比语言-图像预训练）的多模态描述能力，为每个地理图像特征创建一组可学习的文本提示以形成模糊描述。随后，通过动态文本提示辅助图像编码器的训练，使图像编码器学习到更优且更具泛化性的视觉特征。这种将文本应用于纯视觉任务的策略，解决了多模态模型处理地理图像时因缺乏精确描述而难以广泛应用的挑战。我们在多个大规模视觉地理定位数据集上验证了所提策略的有效性，该方法在多个视觉地理定位数据集上取得了具有竞争力的结果。我们的代码与模型公开于https://github.com/Chain-Mao/ProGEO。