AI system ArtAnno enhances artwork annotation through human-AI collaboration

Researchers have developed ArtAnno, a novel system designed to improve the annotation of implicit semantics in artworks. This system utilizes a bidirectional human-AI augmentation framework where an AI agent proactively suggests semantic labels and mines information, while human annotators continuously refine the AI's knowledge base through their expertise. A user study with 20 annotators and two case studies indicated that ArtAnno enhances annotation efficiency, facilitates knowledge accumulation, and reduces the effort required for information seeking and verification, particularly for annotators with less domain-specific knowledge. AI

IMPACT This framework could streamline the process of creating datasets for computational art research, potentially accelerating advancements in AI's understanding and analysis of art.

RANK_REASON The cluster contains an academic paper detailing a new research framework and system for AI-assisted annotation. [lever_c_demoted from research: ic=1 ai=1.0]

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• ArtAnno
• Bidirectional Human-AI Augmentation
• LLM

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