标题： 基于提示的多模态语义通信用于多光谱图像分割 显示英文标题

标题： Prompt-based Multimodal Semantic Communication for Multi-spectral Image Segmentation

摘要： 多模态语义通信因其能够提升下游任务性能而受到广泛关注。 此类系统中的一个关键挑战是有效融合不同模态的特征，这需要从每个模态中提取丰富且多样的语义表示。 为此，我们提出了ProMSC-MIS，一种基于提示的多光谱图像分割多模态语义通信系统。 具体而言，我们提出了一种预训练算法，其中来自一个模态的特征作为另一个模态的提示，引导单模态语义编码器学习多样且互补的语义表示。 我们进一步引入了一个语义融合模块，结合交叉注意力机制和挤压-激励（SE）网络以有效地融合跨模态特征。 仿真结果表明，ProMSC-MIS在各种信道-源压缩级别上显著优于基准方法，同时保持低计算复杂度和存储开销。 我们的方案在自动驾驶和夜间监控等应用中具有很大的潜力。 显示英文摘要

摘要： Multimodal semantic communication has gained widespread attention due to its ability to enhance downstream task performance. A key challenge in such systems is the effective fusion of features from different modalities, which requires the extraction of rich and diverse semantic representations from each modality. To this end, we propose ProMSC-MIS, a Prompt-based Multimodal Semantic Communication system for Multi-spectral Image Segmentation. Specifically, we propose a pre-training algorithm where features from one modality serve as prompts for another, guiding unimodal semantic encoders to learn diverse and complementary semantic representations. We further introduce a semantic fusion module that combines cross-attention mechanisms and squeeze-and-excitation (SE) networks to effectively fuse cross-modal features. Simulation results show that ProMSC-MIS significantly outperforms benchmark methods across various channel-source compression levels, while maintaining low computational complexity and storage overhead. Our scheme has great potential for applications such as autonomous driving and nighttime surveillance.