ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization (2406.01906v1)

Abstract: 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.

ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization

The paper introduces ProGEO, an innovative approach aimed at enhancing the precision and applicability of visual geo-localization, a crucial task in fields such as robotics, computer vision, and geographic information systems. ProGEO addresses the inherent challenges of fine-grained geographic image analysis, which often suffers from a lack of detailed textual descriptions that can aid in accurate localization.

ProGEO employs a two-stage training methodology grounded in multi-modal image-text contrastive learning, leveraging the versatility of the CLIP model. This model is well-regarded for its ability to align visual and textual representations effectively. The first stage of training involves crafting vague text descriptions for geographic image features using learnable text prompts. This stage focuses on mining stored hidden states from both image and text encoders of CLIP while maintaining the integrity of its multimodal capabilities. Optimizing a contrastive loss function, ProGEO fosters a stronger alignment between visual and textual embeddings.

In the subsequent training stage, the system utilizes these text prompts to refine the image encoder. This stage integrates the benefits of geometric metric learning techniques like triplet loss, enhancing the robustness and detail orientation of extracted image features. The integration of category-based training strategies, as exemplified by CosPlace, facilitates a structured approach to learning, enabling more robust localization.

The experimental validation of ProGEO demonstrates its effectiveness across several large-scale visual geo-localization datasets, including the Pitts30k, St Lucia, and others. It employs rigorous metrics like R@1 and R@5 to quantify performance, achieving superior generalization and accuracy compared to existing methods. Notably, ProGEO significantly outperforms alternatives such as NetVLAD, GeM, and various recent vision-language integration techniques, achieving notable performance metrics across multiple datasets.

The implications of this research are multifaceted. Practically, ProGEO promises enhanced performance for applications requiring precise geo-localization, like autonomous navigation systems, urban planning, and augmented reality interfaces. Theoretically, it opens new avenues for integrating multi-modal data to solve visual tasks, underscoring the potential for further advancement in contrastive learning frameworks.

The fusion of learnable prompts and metric learning within a two-stage design positions ProGEO as a vital contribution to the field, illustrating the benefits of leveraging CLIP's multimodal alignment capabilities. Future work could explore the scalability of ProGEO across other visual domains, as well as its adaptability in real-time applications. Moreover, additional research could investigate the refinement of prompt generation to facilitate even more nuanced image feature extraction, enhancing the model's application across broader datasets.