InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images
This addresses the problem of continuous learning in remote sensing object detection for applications like surveillance and mapping, but it is incremental as it builds on existing few-shot detection methods.
The paper tackles incremental few-shot object detection in remote sensing images by introducing InfRS, a fine-tuning-based method that learns new classes with limited examples while maintaining base class performance, achieving effective results on NWPU VHR-10 and DIOR datasets.
Recently, the field of few-shot detection within remote sensing imagery has witnessed significant advancements. Despite these progresses, the capacity for continuous conceptual learning still poses a significant challenge to existing methodologies. In this paper, we explore the intricate task of incremental few-shot object detection in remote sensing images. We introduce a pioneering fine-tuningbased technique, termed InfRS, designed to facilitate the incremental learning of novel classes using a restricted set of examples, while concurrently preserving the performance on established base classes without the need to revisit previous datasets. Specifically, we pretrain the model using abundant data from base classes and then generate a set of class-wise prototypes that represent the intrinsic characteristics of the data. In the incremental learning stage, we introduce a Hybrid Prototypical Contrastive (HPC) encoding module for learning discriminative representations. Furthermore, we develop a prototypical calibration strategy based on the Wasserstein distance to mitigate the catastrophic forgetting problem. Comprehensive evaluations on the NWPU VHR-10 and DIOR datasets demonstrate that our model can effectively solve the iFSOD problem in remote sensing images. Code will be released.