Balancing Attention to Base and Novel Categories for Few-Shot Object Detection in Remote Sensing Imagery

Few-shot object detection (FSOD) has garnered widespread attention in recent years, which makes it possible to learn novel classes with only a handful of labeled samples. Due to the obvious long-tail distribution of remote sensing (RS) data and serious challenges in data labeling, FSOD holds greater practical application value in RS. At present, the FSOD algorithms with fine-tuning of some parameters have attracted much attention due to their stronger incremental learning capacity. However, given large intraclass scale variations and small interclass feature differences of RS objects, we still need to place greater emphasis on the localization and classification of objects. In this article, we introduce the RoI feature refinement (RIFR) method for FSOD in RS imagery, which adopts a powerful training pipeline to better balance attention to the performance of base and novel classes. Aiming at large intraclass scale variations of RS objects, we design a scale-aware feature compensation module (SAFCM). By compensating for insufficient scale information, the model’s ability to perceive scale variations of the same class objects has been enhanced. Considering the high similarity among RS classes, we come up with a prototype trihard (PT) loss. It achieves the effect of interclass separability by constraining the relationship between samples and prototypes. Thus, the issue of interclass confusion has been resultfully solved. Comprehensive experiments on three datasets, DIOR, NWPU VHR-10.v2, and FAIR1M-Airplane, can showcase the efficacy of our RIFR method, and it can implement the most outstanding performance currently. The code will be available at: https://github.com/ningerhhh/RIFR.