Mutual Guidance meets Supervised Contrastive Learning: Vehicle Detection in Remote Sensing Images

A garden enclosed, my sister, my bride, a garden enclosed, a fountain sealed!
Your branches are a grove of pomegranates, with all choicest fruits
(Songs of Songs, 4:12-13)

Abstract

Vehicle detection is an important but challenging problem in Earth observation due to the intricately small sizes and varied appearances of the objects of interest. In this paper, we use these issues to our advantage by considering them results of latent image augmentation. In particular, we propose using supervised contrastive loss in combination with a mutual guidance matching process to helps learn stronger object representations and tackles the misalignment of localization and classification in object detection. Extensive experiments are performed to understand the combination of the two strategies and show the benefits for vehicle detection on aerial and satellite images, achieving performance on par with state-of-the-art methods designed for small and very small object detection. As the proposed method is domain-agnostic, it might also be used for visual representation learning in generic computer vision problems. Source code will be released upon acceptance to facilitate reproduction.

Paper

WACV | arxiv

Citation

If you find the material useful please consider citing our work

@article{le22cmgsrs,
 author = {L{\^{e}}, Ho{\`{a}}ng{-}{\^{A}}n and Zhang, Heng and Pham, Minh-Tan
 and Lefèvre, Sébastien},
 title = {{Mutual Guidance meets Supervised Contrastive Learning: Vehicle Detection
in Remote Sensing Images}},
 booktitle = {Remote Sensing},
 year = {2022},
}