VFM-Loc: Zero-Shot Cross-View Geo-Localization via Aligning Discriminative Visual Hierarchies

• URL: http://arxiv.org/abs/2603.13855v1
• Date: Sat, 14 Mar 2026 09:23:59 GMT
• Title: VFM-Loc: Zero-Shot Cross-View Geo-Localization via Aligning Discriminative Visual Hierarchies
• Abstract summary: Cross-View Geo-Localization (CVGL) in remote sensing aims to locate a drone-view query by matching it to geo-tagged satellite images.<n>We present VFM-Loc, a training-free framework for zero-shot CVGL that leverages the generalizable visual representations from vision foundational models (VFMs)<n>Experiments demonstrate that VFM-Loc exhibits strong zero-shot accuracy on standard benchmarks and surpasses supervised methods by over 20% in Recall@1 on the challenging LO-UCV dataset with large oblique angles.
• Score: 5.868908918033978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-View Geo-Localization (CVGL) in remote sensing aims to locate a drone-view query by matching it to geo-tagged satellite images. Although supervised methods have achieved strong results on closeset benchmarks, they often fail to generalize to unconstrained, real-world scenarios due to severe viewpoint differences and dataset bias. To overcome these limitations, we present VFM-Loc, a training-free framework for zero-shot CVGL that leverages the generalizable visual representations from vision foundational models (VFMs). VFM-Loc identifies and matches discriminative visual clues across different viewpoints through a progressive alignment strategy. First, we design a hierarchical clue extraction mechanism using Generalized Mean pooling and Scale-Weighted RMAC to preserve distinctive visual clues across scales while maintaining hierarchical confidence. Second, we introduce a statistical manifold alignment pipeline based on domain-wise PCA and Orthogonal Procrustes analysis, linearly aligning heterogeneous feature distributions in a shared metric space. Experiments demonstrate that VFM-Loc exhibits strong zero-shot accuracy on standard benchmarks and surpasses supervised methods by over 20% in Recall@1 on the challenging LO-UCV dataset with large oblique angles. This work highlights that principled alignment of pre-trained features can effectively bridge the cross-view gap, establishing a robust and training-free paradigm for real-world CVGL. The relevant code is made available at: https://github.com/DingLei14/VFM-Loc.

Related papers

• Sheaf-Based Federated Representation Learning [11.430396441085579]
  Sheaf-based Federated Representation Learning (SFRL)<n>We develop a decentralized algorithm for solving SFRL, termed Sheaf-FRL, which alternates between gradient updates of the local models and closed-form Procrustes updates of the edge-wise restriction maps.<n>As an application, we consider a cooperative classification task in the context of semantic communication.
  arXiv  Detail & Related papers  (2026-08-08T16:38:34Z)
• BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization [50.74663742490919]
  Cross-View Geo-Localization (CVGL) aims to acquire the geolocation of images by image retrieval.<n>This paper proposes a parameter-efficient framework for bridging the geometric gap across images based on the vision foundation model (VFM)<n>It mainly contains a Multi-granularity Feature Enhancement Adapter (MFEA) and a Frequency-Aware Structural Aggregation (FASA) module.
  arXiv  Detail & Related papers  (2026-05-11T10:46:33Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• TagaVLM: Topology-Aware Global Action Reasoning for Vision-Language Navigation [70.23578202012048]
  Vision-Language Navigation (VLN) presents a unique challenge for Large Vision-Language Models (VLMs) due to their inherent architectural mismatch.<n>We propose TagaVLM (Topology-Aware Global Action reasoning), an end-to-end framework that explicitly injects topological structures into the VLM backbone.<n>To enhance topological node information, an Interleaved Navigation Prompt strengthens node-level visual-text alignment.<n>With the embedded topological graph, the model is capable of global action reasoning, allowing for robust path correction.
  arXiv  Detail & Related papers  (2026-03-03T13:28:07Z)
• Object Detection as an Optional Basis: A Graph Matching Network for Cross-View UAV Localization [17.908597896653045]
  This paper presents a cross-view UAV localization framework that performs map matching via object detection.<n>In typical pipelines, UAV visual localization is formulated as an image-retrieval problem.<n>Our method achieves strong retrieval and localization performance using a fine-grained, graph-based node-similarity metric.
  arXiv  Detail & Related papers  (2025-11-04T11:25:31Z)
• GLEAM: Learning to Match and Explain in Cross-View Geo-Localization [66.11208984986813]
  Cross-View Geo-Localization (CVGL) focuses on identifying correspondences between images captured from distinct perspectives of the same geographical location.<n>We present GLEAM-C, a foundational CVGL model that unifies multiple views and modalities-including UAV imagery, street maps, panoramic views, and ground photographs-by aligning them exclusively with satellite imagery.<n>To address the lack of interpretability in traditional CVGL methods, we propose GLEAM-X, which combines cross-view correspondence prediction with explainable reasoning.
  arXiv  Detail & Related papers  (2025-09-09T07:14:31Z)
• OSMLoc: Single Image-Based Visual Localization in OpenStreetMap with Fused Geometric and Semantic Guidance [20.043977909592115]
  OSMLoc is a brain-inspired visual localization approach based on first-person-view images against the OpenStreetMap maps.<n>It integrates semantic and geometric guidance to significantly improve accuracy, robustness, and generalization capability.
  arXiv  Detail & Related papers  (2024-11-13T14:59:00Z)
• Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization [28.941724648519102]
  This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL) Common approaches to CVGL rely on ground-satellite image pairs and employ label-driven supervised training. We propose an unsupervised framework including a cross-view projection to guide the model for retrieving initial pseudo-labels.
  arXiv  Detail & Related papers  (2024-03-21T07:48:35Z)
• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Co-visual pattern augmented generative transformer learning for automobile geo-localization [12.449657263683337]
  Cross-view geo-localization (CVGL) aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial images. We present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL) for CVGL.
  arXiv  Detail & Related papers  (2022-03-17T07:29:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.