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20162024
most citedE(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition

4 citations · 18 across the 19 of their papers we have counts for

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19 papers · 1 filter

cs.CV20241 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

The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement

Gabriele Trivigno, Carlo Masone, Barbara Caputo +1

Pose refinement is an interesting and practically relevant research direction. Pose refinement can be used to (1) obtain a more accurate pose estimate from an initial prior (e.g.,…

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…

cs.CV20243 cited

Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model

Claudia Cuttano, Antonio Tavera, Fabio Cermelli +2

Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware. Distillation from a trained source…

cs.CV20231 cited

FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving

Eros Fanì, Marco Ciccone, Barbara Caputo

We propose FedDrive v2, an extension of the Federated Learning benchmark for Semantic Segmentation in Autonomous Driving. While the first version aims at studying the effect of dom…