FAPER 2025 - 4th International Workshop for Fine Art Pattern Extraction and Recognition

About the Workshop

The 4th International Workshop on Fine Art Pattern Extraction and Recognition (FAPER 2025) aims to provide an interdisciplinary forum for researchers, practitioners, and industry professionals working at the intersection of computer vision, artificial intelligence, and cultural heritage. The workshop seeks to bridge the gap between computational methods and the humanities, fostering collaboration between AI researchers, art historians, and museum professionals.

We invite submissions on innovative research, ongoing projects, and industrial applications related to AI and computer vision in fine arts and cultural heritage. The goal is to advance state-of-the-art methodologies while ensuring that AI solutions align with the analytical depth expected in the humanities and cultural preservation sectors.

Topics of Interest

Topics of interest include, but are not limited to:

Machine learning and deep learning for cultural heritage and digital humanities: Application of AI-driven techniques to analyze, restore, and preserve cultural artifacts, manuscripts, and artworks.
Creative AI and computational creativity: AI-driven art generation, style transfer, and computational methods for artistic interpretation and synthesis.
Multimodal AI for art understanding: Integrating vision-language models for semantic understanding, storytelling, and interactive experiences in the art domain.
Generative AI for artistic style transfer and restoration: Leveraging generative models to reconstruct damaged artworks, restore missing details, and generate plausible stylistic variations.
Explainable AI for art and cultural analysis: Developing interpretable AI models that provide historically and culturally meaningful justifications for their predictions and insights.
AI-Driven art provenance and authentication: Using machine learning to detect forgeries, track artwork provenance, and analyze historical attributions in art history.
Interactive AI and conversational agents for museums and galleries: Deploying LLM-powered virtual guides and chatbots to enable personalized and immersive museum experiences.
Personalized art recommendation systems using deep learning: AI-driven recommender systems that suggest artworks based on user preferences, historical data, and stylistic analysis.
3Dreconstruction and point cloud processing of historical artifacts: Digitization, modeling, and analysis of sculptures, monuments, and historical structures.
Content-based image retrieval and search in the art domain: Advanced retrieval models for searching and identifying artworks based on visual and semantic features.
Speech, audio, and music analysis from historical archives: AI methodologies for analyzing historical audio recordings, musical compositions, and spoken-word archives.
Arthistory and computer vision: Computational techniques for stylistic, iconographic, and iconological analysis of fine art.
Projects, products, and prototypes for cultural heritage restoration, preservation, and engagement: Real-world applications and technological solutions for conserving and sharing cultural heritage assets.