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Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization

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arxiv 2302.01572 v1 pith:L3O4SPWE submitted 2023-02-03 cs.CV

Yingying Zhu , Hongji Yang , Yuxin Lu , Qiang Huang This is my paper · ORCID
classification cs.CV
keywords saigcross-viewfeaturegeo-localizationbackboneimagesimpleeffective
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In this work, we aim at an important but less explored problem of a simple yet effective backbone specific for cross-view geo-localization task. Existing methods for cross-view geo-localization tasks are frequently characterized by 1) complicated methodologies, 2) GPU-consuming computations, and 3) a stringent assumption that aerial and ground images are centrally or orientation aligned. To address the above three challenges for cross-view image matching, we propose a new backbone network, named Simple Attention-based Image Geo-localization network (SAIG). The proposed SAIG effectively represents long-range interactions among patches as well as cross-view correspondence with multi-head self-attention layers. The "narrow-deep" architecture of our SAIG improves the feature richness without degradation in performance, while its shallow and effective convolutional stem preserves the locality, eliminating the loss of patchify boundary information. Our SAIG achieves state-of-the-art results on cross-view geo-localization, while being far simpler than previous works. Furthermore, with only 15.9% of the model parameters and half of the output dimension compared to the state-of-the-art, the SAIG adapts well across multiple cross-view datasets without employing any well-designed feature aggregation modules or feature alignment algorithms. In addition, our SAIG attains competitive scores on image retrieval benchmarks, further demonstrating its generalizability. As a backbone network, our SAIG is both easy to follow and computationally lightweight, which is meaningful in practical scenario. Moreover, we propose a simple Spatial-Mixed feature aggregation moDule (SMD) that can mix and project spatial information into a low-dimensional space to generate feature descriptors... (The code is available at https://github.com/yanghongji2007/SAIG)

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Warp-free Cross-view Geo-localization via Feature-space Consensus Mining

    cs.CV 2026-08 conditional novelty 7.0 of 10

    A training-only joint-view branch that mines semantic consensus between street and satellite images, distilled into single-view encoders, improves cross-view geo-localization retrieval without extra test-time cost.

  2. GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching

    cs.CV 2025-05 conditional novelty 6.0 of 10

    GeoVLM reranks top-10 candidates from a pretrained cross-view encoder by fusing image and text embeddings, improving top-1 retrieval on VIGOR, CVUK, and University-1652.

  3. A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios

    cs.CV 2025-05 conditional novelty 6.0 of 10

    UnifyGeo unifies retrieval and metric localization in one shared-encoder network, improving 1-meter-level geo-localization recall on VIGOR from 1.53% to 39.64% in same-area evaluation.

  4. Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings

    cs.CV 2024-12 conditional novelty 6.0 of 10

    The authors introduce the decentrality problem in cross-view geo-localization and present DReSS, a worldwide dataset with strong query-reference offsets, plus AuxGeo, an auxiliary-task method that achieves state-of-th...

  5. DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Combining MLLM candidate place extraction with DINOv2 cross-view matching of social-media, street-view, and satellite images raises disaster photo geolocation accuracy to 71.6% within 1 km on Hurricane Harvey data.

  6. OffNadirLoc: Benchmark and Framework for Challenging UAV-to-Satellite Geo-Localization under Large Off-Nadir Views

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A new benchmark and framework for matching sharply angled UAV photos to satellite maps, with an aggregation and group-learning method that outperforms prior work on most tested datasets.

  7. Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization

    cs.CV 2025-06 conditional novelty 5.0 of 10

    The authors propose distance-aware cross-view geo-localization, release the DA-Campus benchmark, and show a multi-scale contrastive loss with re-ranking improves both hierarchical and standard retrieval.

  8. Retrieval-guided Cross-view Image Synthesis

    cs.CV 2024-11 conditional novelty 5.0 of 10

    Cross-view image synthesis is improved by conditioning a GAN on a pre-trained retrieval embedding and an identity loss, plus a new urban dataset VIGOR-GEN.

  9. CV-Cities: Advancing Cross-View Geo-Localization in Global Cities

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A DINOv2-based framework with feature mixing and hard negative sampling achieves state-of-the-art cross-view geo-localization, supported by a new global dataset, CV-Cities.

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