TY - GEN

T1 - Cross-dataset Learning for Generalizable Land Use Scene Classification

AU - Gominski, Dimitri

AU - Gouet-Brunet, Valerie

AU - Chen, Liming

N1 - Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or un-seen visual distributions, but are hardly applicable to real-world situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach1 exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

AB - Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or un-seen visual distributions, but are hardly applicable to real-world situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach1 exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

U2 - 10.1109/CVPRW56347.2022.00144

DO - 10.1109/CVPRW56347.2022.00144

M3 - Article in proceedings

AN - SCOPUS:85137790889

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

SP - 1381

EP - 1390

BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022

PB - IEEE Computer Society Press

T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022

Y2 - 19 June 2022 through 20 June 2022

ER -