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

TL;DR

ProGEO introduces a two-stage contrastive learning approach leveraging multi-modal prompts to improve visual geo-localization accuracy, addressing the challenge of fine-grained image features and limited descriptions.

Contribution

It proposes a novel method using learnable text prompts and contrastive learning to enhance visual feature extraction for geo-localization tasks.

Findings

Achieves competitive results on large-scale geo-localization datasets.

Demonstrates the effectiveness of multi-modal prompts in improving visual feature learning.

Validates the approach's generalizability across multiple datasets.

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.