SAMA: Semantic Anchor-aligned Augmentation for Unified Low-Resource Multimodal Information Extraction

2026-06-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Semantic Anchor-aligned Multimodal Augmentation (SAMA) is a unified framework addressing severe data scarcity in Multimodal Information Extraction (MIE) tasks, including Multimodal Named Entity Recognition (MNER), Relation Extraction (MRE), and Event Extraction (MEE). SAMA generates high-fidelity, task-aware synthetic data by constructing structured semantic anchors from ground-truth labels. It guides a Collaborative Multi-Experts Multimodal Large Language Model (CME-MLLM) with Universal and Task-Specific Adapters for textual samples. For image synthesis, SAMA employs an Anchor-Preserving Diffusion mechanism using anchor-weighted prompts and latent conditioning. A Dual-Constraint Filtering module selects synthetic samples based on cross-modal consistency and anchor fidelity, eliminating manual verification. Experiments show SAMA consistently outperforms state-of-the-art augmentation baselines in both fully supervised and low-resource settings.

Key takeaway

For machine learning engineers developing multimodal information extraction systems facing data scarcity, you should consider integrating Semantic Anchor-aligned Multimodal Augmentation (SAMA). This framework offers a robust solution for generating high-fidelity synthetic data across MNER, MRE, and MEE tasks, significantly improving performance in low-resource settings. Its automated dual-constraint filtering eliminates the need for manual verification, streamlining your data augmentation pipeline.

Key insights

SAMA unifies multimodal data augmentation using semantic anchors to generate high-fidelity, task-aware synthetic data.

Principles

• Cross-modal alignment is critical for effective MIE data augmentation.
• Unified frameworks exploit shared semantics across MIE tasks.
• Anchor-guided generation improves synthetic data fidelity and diversity.

Method

SAMA constructs semantic anchors, guides a CME-MLLM for text, employs Anchor-Preserving Diffusion for images, and filters samples via a Dual-Constraint module for consistency and fidelity.

In practice

• Generate synthetic data for MNER, MRE, and MEE tasks.
• Overcome data scarcity in multimodal information extraction.
• Automate synthetic data verification with dual constraints.

Topics

• Multimodal Information Extraction
• Data Augmentation
• Low-Resource Learning
• Semantic Anchors
• Multimodal Large Language Models
• Diffusion Models

Best for: Research Scientist, AI Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.