MegaRAG: Build Multimodal RAG That Understands Charts & Slides Like a Human
1 months ago 高效码农MegaRAG: Teaching RAG to Read Diagrams, Charts, and Slide Layouts Like a Human “ What makes MegaRAG different? It treats every page as a mini-multimodal graph—text, figures, tables, and even the page screenshot itself become nodes. A two-pass large-language-model pipeline first extracts entities in parallel, then refines cross-modal edges using a global subgraph. The final answer is produced in two stages to prevent modality bias. On four public benchmarks the system outperforms GraphRAG and LightRAG by up to 45 percentage points while running on a single RTX-3090. § The Core Question This Article Answers “How can I build a retrieval-augmented-generation …
Visual Question Answering Breakthrough: How NoteMR Enhances Multimodal Model Reasoning
7 months ago 高效码农Breaking the Cognitive Boundaries of Visual Question Answering: How Knowledge and Visual Notes Enhance Multimodal Large Model Reasoning Introduction: The Cognitive Challenges of Visual Question Answering In today’s information explosion era, visual question answering (VQA) systems need to understand image content and answer complex questions like humans. However, existing multimodal large language models (MLLMs) often face two core challenges when dealing with visual problems requiring external knowledge: 1.1 Limitations of Traditional Methods Traditional knowledge-based visual question answering (KB-VQA) methods mainly fall into two categories: Explicit retrieval methods: Rely on external knowledge bases but introduce noisy information Implicit LLM methods: Utilize …