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 language | English |
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Title of host publication | Computer Vision – ECCV 2024 |
Subtitle of host publication | 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XXXI |
Editors | Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol |
Place of Publication | Cham |
Publisher | Springer |
Pages | 397-415 |
Number of pages | 19 |
ISBN (Electronic) | 978-3-031-72751-1 |
ISBN (Print) | 978-3-031-72750-4 |
DOIs | |
Publication status | Published - 2025 |
Event | European Conference on Computer Vision – ECCV 2024 - MiCo Milano, Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024 https://eccv.ecva.net/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Publisher | Springer |
Volume | 15089 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | European Conference on Computer Vision – ECCV 2024 |
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Abbreviated title | ECCV 2024 |
Country/Territory | Italy |
City | Milan |
Period | 29/09/24 → 4/10/24 |
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-careOtherwise 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.