Multimodal Mutual Learning with Online Knowledge Distillation for Dim Object Recognition in Aerial Images

Deep learning methods have shown promise in various visual tasks such as object recognition. However, achieving robust and accurate performance in dim object recognition for remote sensing images remains challenging in the field of computer vision. This challenge can be attributed to factors such as cluttered backgrounds, varying observing angles, and limited availability of labeled data. In contrast, the human brain exhibits robust and efficient recognition of sensitive targets. To leverage the strengths of both computer calculation and human cognition, we propose a multimodal mutual learning with online knowledge distillation method (MMOKD) for object recognition. Our approach enables simultaneous training and mutual learning between modalities, where each modality serves as both a teacher and a student. A series of experiments are conducted to verify the potential of multimodal learning for object recognition. The results demonstrate that our approach not only enhances the robustness of multimodal fusion model, but also improves the accuracy of visual modality.