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This work was supported in part by the State Key Program of the National Natural Science Foundation of China under Grant 42530109 and in part by the Henan Provincial Natural Science Foundation under Grant 252300423933. (Corresponding authors: Juepeng Zheng and Jianxi Huang.)
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Achieve state-of-the-art performance in multimodal remote sensing semantic segmentation with significantly fewer trainable parameters by using a novel parameter-efficient and modality-balanced symmetric fusion framework.
