Dynamic Interactive Learning for Lightweight Detectors in Remote Sensing Imagery

Abstract

The lightweight model has played an important role in the remote sensing (RS) realm. The existing researchers have proposed many models with lightweight structures, but their performance still has a gap compared with the deep model. A promising approach to optimize the lightweight model is knowledge distillation (KD), which can be viewed as knowledge transfer from the teacher model. However, the existing KD approaches have some issues. On one hand, offline distillation methods usually ignore the interactive learning between the student model and the teacher model. On the other hand, knowledge transfer does not consider instance property. This offline distillation strategy without property perception may not suitable for multiscale, diverse, and complex RS instances and results in a suboptimum training status. In this article, we propose a dynamic interactive learning (DIL) framework for optimizing RS lightweight detectors. First, we propose an instance interaction learning module. It calculates the value of every instance in the batch of the teacher and student prediction by each model’s real-time state and instance property. Then according to the DIL thought, we facilitate the low-quality instance to learn from the high-quality one whether it is from the teacher or student model. Moreover, we also propose the instance property perception (IPP) strategy that weighs the distillation knowledge of instances according to their feature, category, and location property. In the proposed DIL framework, both the teacher and student models are trained together and it is cost-free in the testing phase. Extensive experiments on three RS datasets demonstrate the effectiveness of the DIL.