基于知识蒸馏的轻量化遥感多模态大语言模型OA

Remote sensing multimodal large language models(MLLMs),which integrate rich visual-linguistic modal in‐formation,have shown great potential in areas such as remote sensing image analysis and interpretation.However,exist‐ing knowledge distillation methods primarily focus on the compression of unimodal large language models,neglecting the alignment of features across modalities,thus hindering the performance of large language models in cross-modal tasks.To address this issue,a lightweighting method for remote sensing MLLMs based on knowledge distillation is pro‐posed.This method achieves effective alignment of multimodal information by aligning the outputs across modalities at the feature level.By introducing the reverse Kullback-Leibler divergence as the loss function and combining optimiza‐tion strategies such as teacher mixed sampling and single-step decomposition,the generalization and stability of the stu‐dent model are further enhanced.Experimental results demonstrate that the proposed method achieves higher accuracy and efficiency in four downstream tasks of remote sensing image scene classification,visual question answering,visual localization,and image description,significantly reducing the number of model parameters and the demand for computa‐tional resources,thereby providing a new solution for the efficient application of MLLMs in the field of remote sensing.