MAPS: Multi-Anchor Projection Similarity for Joint Vision-Language Geo-Localization

2026-06-21

AI Summary

This work addresses the limitations of existing vision-language geolocation methods, which rely on point-to-point alignment and struggle to effectively model the semantic subspace jointly defined by images and text. To overcome this, the paper formulates the task as a multi-anchor geometric alignment problem and introduces a novel Multi-Anchor Projection Similarity (MAPS) metric. MAPS measures cross-modal similarity through the projection length of a target feature onto an anchor plane spanned by image and text features, thereby surpassing the constraints of conventional cosine similarity. Correspondingly, a MAPS contrastive loss is designed to enable joint representation learning in high-dimensional space. The proposed approach achieves state-of-the-art performance on vision-language geolocation benchmarks, significantly improving cross-modal retrieval accuracy.

Abstract

Humans localize places by integrating perceptual cues from vision with semantic reasoning from language, forming a scene understanding that is both intuitive and structured. Although existing geo-localization models have made substantial progress in cross-view and cross-modal settings, they are largely built upon point-to-point alignment, which is insufficient for joint vision-language queries. In such queries, visual and textual cues do not simply act as independent references, but jointly define a semantic subspace for locating the target. In this paper, we formulate vision-language geo-localization (VLGL) with joint image-text queries as a multi-anchor geometric alignment problem and propose a unified framework for this setting. To realize this formulation, we propose Multi-Anchor Projection Similarity (MAPS), a new metric which constructs an anchor plane from visual and textual query features in a high-dimensional space and measures similarity by the projection length of the target feature onto this plane. Unlike cosine similarity which evaluates isolated pairwise relations, MAPS captures the geometric consistency between the target feature and the joint query subspace, providing a more discriminative ranking criterion during retrieval. To make the learned representation consistent with this geometry, we further introduce a MAPS-based contrastive loss that drives target features toward the corresponding anchor plane. The proposed framework, similarity metric, and training objective jointly yield state-of-the-art performance in VLGL.

Problem

• vision-language geo-localization
• joint image-text queries
• semantic subspace
• cross-modal alignment
• geometric consistency

Innovation

• Multi-Anchor Projection Similarity
• Vision-Language Geo-Localization
• Geometric Alignment
• Contrastive Learning
• Semantic Subspace