MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

1. 1Construct a multi-granularity graph to capture structural dependencies between modalities
2. 2Use the graph to perform retrieval-augmented generation, mitigating hallucinations in multimodal large language models
3. 3Fine-tune the model on specific tasks to adapt to different cross-modal reasoning requirements
4. 4Evaluate the model's performance on benchmarks to assess its effectiveness in multimodal retrieval-augmented generation

AI engineers and researchers working on multimodal large language models can benefit from this approach to improve the performance of their models, particularly in tasks that require complex cross-modal reasoning

💡 Using a multi-granularity graph can help capture fine-grained visual information and improve the performance of multimodal large language models

🚀 MG$^2$-RAG: A new multimodal retrieval-augmented generation model that uses a multi-granularity graph to improve cross-modal reasoning!