Frequency-Aware Vision-Language Multimodality Generalization Network for Remote Sensing Image Classification

The paper presents a new framework called Missing-aware Mixture-of-Loras (MaMOL) aimed at improving multimodal classification in remote sensing, which often faces challenges due to missing modalities from environmental factors or sensor failures. The proposed method reformulates the issue as a multi-task learning problem, utilizing a dual-routing mechanism to enhance adaptability and knowledge sharing among experts. This approach promises to improve classification performance significantly by addressing the limitations of existing methods that rely on complete multimodal data during training.