Reproduce “M4SC: An MLLM-based Multi-modal, Multi-task and Multi-user Semantic Communication System” on OpenResearch
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About this paper
Feibo Jiang, Siwei Tu, Li Dong, Kezhi Wang, Kun Yang, Cunhua Pan
The M4SC system integrates a tailored Multi-modal Large Language Model (MLLM) with a KAN-based cross-modal projector and multi-task instruction fine-tuning to address multi-modal, multi-task, and multi-user semantic communication challenges, demonstrating improved accuracy in multi-modal VQA and text classification, alongside substantial bandwidth optimization in multi-user scenarios.
Key insights
- Tailored MLLMs, when combined with specialized neural architectures like KAN, can significantly enhance cross-modal semantic understanding and alignment in communication systems.
- Multi-task instruction fine-tuning allows a single semantic communication system to adapt to diverse tasks while mitigating catastrophic forgetting.
- Identifying and sharing common "public" semantic information among multiple users dramatically reduces redundant data transmission, optimizing bandwidth efficiency.
Summary by alphaXiv.
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