Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation
Zechen Bai, Yuta Nakashima, Noa Garcia
摘要
Have you ever looked at a painting and wondered what is the story behind it? This work presents a framework to bring art closer to people by generating comprehensive descriptions of fine-art paintings. Generating informative descriptions for artworks, however, is extremely challenging, as it requires to 1) describe multiple aspects of the image such as its style, content, or composition, and 2) provide background and contextual knowledge about the artist, their influences, or the historical period. To address these challenges, we introduce a multi-topic and knowledgeable art description framework, which modules the generated sentences according to three artistic topics and, additionally, enhances each description with external knowledge. The framework is validated through an exhaustive analysis, both quantitative and qualitative, as well as a comparative human evaluation, demonstrating outstanding results in terms of both topic diversity and information veracity.
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引用它的顶会 Paper10
- One Token to Seg Them All: Language Instructed Reasoning Segmentation in VideosZechen Bai, Tong He, Haiyang Mei, Pichao Wang 等NeurIPS 2024 · 被引用 147 次
- ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art UnderstandingShuai Wang, Ivona Najdenkoska, Hongyi Zhu, Stevan Rudinac 等ACM MM 2025 · 被引用 6 次
- Understanding Museum Exhibits using Vision-Language ReasoningAda-Astrid Balauca, Sanjana Garai, Stefan Balauca, Rasesh Udayakumar Shetty 等ICCV 2025 · 被引用 3 次
- VL-KGE: Vision-Language Models Meet Knowledge Graph EmbeddingsAthanasios Efthymiou, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg 等WWW 2026 · 被引用 2 次
- ImageSet2Text: Describing Sets of Images Through TextPiera Riccio, Francesco Galati, Kajetan Schweighofer, Noa Garcia 等AAAI 2026 · 被引用 1 次
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