Researchers have introduced SAMA, a novel framework designed to address data scarcity in Multimodal Information Extraction (MIE) tasks like Named Entity Recognition, Relation Extraction, and Event Extraction. SAMA utilizes structured semantic anchors to guide a Collaborative Multi-Experts Multimodal Large Language Model (CME-MLLM) for generating high-quality synthetic data. The framework incorporates an Anchor-Preserving Diffusion mechanism for image synthesis and a Dual-Constraint Filtering module to ensure the fidelity of generated samples without manual verification. Experiments show SAMA significantly outperforms existing augmentation methods in both fully supervised and low-resource scenarios. AI
IMPACT Enhances data generation for low-resource multimodal AI tasks, potentially improving performance across various extraction applications.
RANK_REASON This is a research paper detailing a new method for multimodal information extraction.
- Anchor-Preserving Diffusion
- Collaborative Multi-Experts Multimodal Large Language Model
- Dual-Constraint Filtering
- Multimodal Event Extraction
- Multimodal Information Extraction
- Multimodal Named Entity Recognition
- Multimodal Relation Extraction
- Sama
- Task-Specific Adapters
- Universal adapter for sensors
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