# Adaptive-Attentive Geolocalization from few queries: a hybrid approach

arXiv: https://arxiv.org/abs/2010.06897 · HTML: https://mlsanity.com/paper/2010.06897/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 30 of 39)

- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition](https://mlsanity.com/paper/2308.10832/) (2023)
- [R2Former: Unified Retrieval and Reranking Transformer for Place Recognition](https://mlsanity.com/paper/2304.03410/) (2023)
- [Deep Visual Geo-localization Benchmark](https://mlsanity.com/paper/2204.03444/) (2022)
- [BoQ: A Place is Worth a Bag of Learnable Queries](https://mlsanity.com/paper/2405.07364/) (2024)
- [CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition](https://mlsanity.com/paper/2402.19231/) (2024)
- [Viewpoint Invariant Dense Matching for Visual Geolocalization](https://mlsanity.com/paper/2109.09827/) (2021)
- [SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs](https://mlsanity.com/paper/2404.00469/) (2024)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Are Local Features All You Need for Cross-Domain Visual Place Recognition?](https://mlsanity.com/paper/2304.05887/) (2023)
- [AddressCLIP: Empowering Vision-Language Models for City-wide Image Address Localization](https://mlsanity.com/paper/2407.08156/) (2024)
- [Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition](https://mlsanity.com/paper/2504.09881/) (2025)
- [EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition](https://mlsanity.com/paper/2405.18065/) (2024)
- [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://mlsanity.com/paper/2502.16601/) (2025)
- [Learning Semantics for Visual Place Recognition through Multi-Scale Attention](https://mlsanity.com/paper/2201.09701/) (2022)
- [EDTformer: An Efficient Decoder Transformer for Visual Place Recognition](https://mlsanity.com/paper/2412.00784/) (2024)
- [MeshVPR: Citywide Visual Place Recognition Using 3D Meshes](https://mlsanity.com/paper/2406.02776/) (2024)
- [Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era](https://mlsanity.com/paper/2511.06024/) (2025)
- [MutualVPR: A Mutual Learning Framework for Resolving Supervision Inconsistencies via Adaptive Clustering](https://mlsanity.com/paper/2412.09199/) (2024)
- [GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics](https://mlsanity.com/paper/2602.12617/) (2026)
- [Visual place recognition for aerial imagery: A survey](https://mlsanity.com/paper/2406.00885/) (2024)
- [Breaking the Frame: Visual Place Recognition by Overlap Prediction](https://mlsanity.com/paper/2406.16204/) (2024)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition](https://mlsanity.com/paper/2307.08417/) (2023)
- [Collaborative Visual Place Recognition through Federated Learning](https://mlsanity.com/paper/2404.13324/) (2024)
- [Towards General Urban Monitoring with Vision-Language Models: A Review, Evaluation, and a Research Agenda](https://mlsanity.com/paper/2510.12400/) (2025)
- [A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition](https://mlsanity.com/paper/2412.06153/) (2024)
- [TeTRA-VPR: A Ternary Transformer Approach for Compact Visual Place Recognition](https://mlsanity.com/paper/2503.02511/) (2025)
- [Artifacts of Idiosyncracy in Global Street View Data](https://mlsanity.com/paper/2505.11046/) (2025)
- [Query-Based Adaptive Aggregation for Multi-Dataset Joint Training Toward Universal Visual Place Recognition](https://mlsanity.com/paper/2507.03831/) (2025)

## Abstract (arXiv)

We address the task of cross-domain visual place recognition, where the goal is to geolocalize a given query image against a labeled gallery, in the case where the query and the gallery belong to different visual domains. To achieve this, we focus on building a domain robust deep network by leveraging over an attention mechanism combined with few-shot unsupervised domain adaptation techniques, where we use a small number of unlabeled target domain images to learn about the target distribution. With our method, we are able to outperform the current state of the art while using two orders of magnitude less target domain images. Finally we propose a new large-scale dataset for cross-domain visual place recognition, called SVOX. The pytorch code is available at https://github.com/valeriopaolicelli/AdAGeo .