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20202026
most citedAdaptive-Attentive Geolocalization from few queries: a hybrid approach

22 citations · 41 across the 22 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CV2024★ 1 cited

MeshVPR: Citywide Visual Place Recognition Using 3D Meshes

Gabriele Berton, Lorenz Junglas, Riccardo Zaccone +3

Mesh-based scene representation offers a promising direction for simplifying large-scale hierarchical visual localization pipelines, combining a visual place recognition step based…

cs.CV2024

Scale-Free Image Keypoints Using Differentiable Persistent Homology

Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno +3

In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scal…

cs.CV2024

EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography

Gabriele Berton, Gabriele Goletto, Gabriele Trivigno +3

Precise, pixel-wise geolocalization of astronaut photography is critical to unlocking the potential of this unique type of remotely sensed Earth data, particularly for its use in d…

cs.CV2024

Collaborative Visual Place Recognition through Federated Learning

Mattia Dutto, Gabriele Berton, Debora Caldarola +3

Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural n…

cs.CV2024

JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition

Gabriele Berton, Gabriele Trivigno, Barbara Caputo +1

Visual Place Recognition aims at recognizing previously visited places by relying on visual clues, and it is used in robotics applications for SLAM and localization. Since typicall…

cs.CV2024

EarthLoc: Astronaut Photography Localization by Indexing Earth from Space

Gabriele Berton, Alex Stoken, Barbara Caputo +1

Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response…