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Source note2 min read

ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation

Source

arXiv

arxiv.org/abs/2608.05026v1

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Source

Plain-Language Brief

Researchers have developed a new system called ArtAnno to help with annotating artworks more efficiently. The system uses a bidirectional Human-AI Augmentation (BiHAA) framework, which allows both the AI and human experts to learn from each other during the annotation process. This is done through a multi-agent architecture, where the AI provides semantic mining and label suggestions, and human expertise is used to continuously improve the AI over time.

What the Document Says

  • Researchers propose Bidirectional Human-AI Augmentation (BiHAA) for annotation of artworks.
  • ArtAnno is an artwork annotation system based on BiHAA.
  • ArtAnno uses a multi-agent architecture that includes Proactive Agentic Support Module and Interaction-Driven Evolution Module.
  • The Proactive Agentic Support Module uses AI for semantic mining and label suggestions.
  • The Interaction-Driven Evolution Module uses human expertise to improve the AI over time.
  • Evaluation shows that ArtAnno improves annotation efficiency, enables knowledge accumulation, and reduces effort for those with limited domain expertise.

Key Concepts Mentioned

  • artwork annotation
  • Bidirectional Human-AI Augmentation (BiHAA)
  • ArtAnno
  • multi-agent architecture
  • Proactive Agentic Support Module
  • Interaction-Driven Evolution Module
  • evaluation

Claims Made by the Document

  • High-quality annotation of artworks is essential for computational art research.
  • Current AI-assisted annotation tools lack assistance or have one-way workflows.
  • BiHAA improves annotation efficiency and enables knowledge accumulation.
  • ArtAnno reduces the effort of information seeking and verification for annotators with limited domain expertise.

Evidence Used

  • The text describes the development of ArtAnno and its benefits in terms of annotation efficiency, knowledge accumulation, and reduced effort for annotators.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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(BiHAA), a closed-loop framework in which skills and domain knowledge base evolve through real-time interaction and bidirectional HAI augmentation. Informed by a formative study with 20 artwork annotators from different backgrounds, we implement this framework in ArtAnno, an artwork annotation system driven by a multi-agent architecture. The system includes a Proactive Agentic Support Module, where AI augments humans through semantic mining and label suggestion, and an Interaction-Driven Evolution Module, where human expertise continuously enhances the AI through distilling annotation trajectories into reusable experience. Evaluation through a user study and two case studies demonstrates that our framework and system improve annotation efficiency, enable knowledge accumulation, and reduce the effort of information seeking and verification for annotators with limited domain expertise. We conclude by discussing broader implications and future directions.