Retentive Compensation and Personality Filtering for Few-Shot Remote Sensing Object Detection

In recent years, few-shot object detection (FSOD) in remote sensing images has attracted increasing attention. Numerous studies address the challenges posed by both intra-class and inter-class variance through strategies such as augmenting sample diversity and incorporating multi-scale features. However, these features still encompass a considerable amount of noise attributes due to the complex characteristic of satellite images, persistently and adversely affecting classification. In contrast, we advocate for the belief that a limited yet refined set of features surpasses a multitude of coarse features. Accordingly, we tackle above issues through the meticulous refinement of representative category features, enhancing performance by eliminating irrelevant attributes that interfere with classification. Specifically, two pivotal modules: retentive compensation module (RCM) and personality filtering module (PFM), are introduced. The former module RCM systematically scrutinizes features proximate to the category center, yielding prototypes that exhibit both intra-class compactness and inter-class distinctiveness. Furthermore, the latter module PFM utilizes previous obtained prototypes to supervise the filtering process, diminishing the intra-class variance by excluding personality features which could impede the classification task. The integration of the above two modules enables a holistic feature representation, capturing inherent similarities within individual classes while accentuating distinctions between classes. Experiments have been conducted on the DIOR and NWPU VHR-10.v2 datasets, and the results demonstrate that our proposed approach exceeds several state-of-the-art methods. Code is available at https://github.com/yomik-js/RP-FSOD.