ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation
High-quality annotation of artworks is essential for computational art research, yet extracting implicit semantics remains challenging due to the reliance on culturally grounded meanings and deep contextual knowledge behind the images. Current AI-assisted annotation tools often lack assistance or rely on one-way workflows where experts have to perform extra manual calibrations to improve AI models, resulting in limited efficiency. To address this, we propose Bidirectional Human-AI Augmentation(B
Record details
Published: 5 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 6 August 2026
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ethics.ai (5 August 2026), “ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation,” evidence record 16924, https://ethics.ai/record/16924 (originally published by arXiv cs.AI).
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