Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Zimin Xia*, Yujiao Shi, Hongdong Li, Julian F.P. Kooij

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on developing advanced networks trained with accurate ground truth (GT) locations of ground images. However, the trained models always suffer a performance drop when applied to images in a new target area that differs from training. In most deployment scenarios, acquiring fine GT, i.e. accurate GT locations, for target-area images to re-train the network can be expensive and sometimes infeasible. In contrast, collecting images with noisy GT with errors of tens of meters is often easy. Motivated by this, our paper focuses on improving the performance of a trained model in a new target area by leveraging only the target-area images without fine GT. We propose a weakly supervised learning approach based on knowledge self-distillation. This approach uses predictions from a pre-trained model as pseudo GT to supervise a copy of itself. Our approach includes a mode-based pseudo GT generation for reducing uncertainty in pseudo GT and an outlier filtering method to remove unreliable pseudo GT. Our approach is validated using two recent state-of-the-art models on two benchmarks. The results demonstrate that it consistently and considerably boosts the localization accuracy in the target area.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2024
Subtitle of host publication18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XXXI
EditorsAleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
Place of PublicationCham
PublisherSpringer
Pages397-415
Number of pages19
ISBN (Electronic)978-3-031-72751-1
ISBN (Print)978-3-031-72750-4
DOIs
Publication statusPublished - 2025
EventEuropean Conference on Computer Vision – ECCV 2024 - MiCo Milano, Milan, Italy
Duration: 29 Sept 20244 Oct 2024
https://eccv.ecva.net/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume15089 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Computer Vision – ECCV 2024
Abbreviated titleECCV 2024
Country/TerritoryItaly
CityMilan
Period29/09/244/10/24
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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