{"type":"ScholarlyArticle","title":"Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories","authors":[{"name":"Xin Jin","slug":"xin-jin","given":"Xin","family":"Jin","affiliation":"Beijing Electronic Science and Technology Institute; Beijing Institute for General Artificial Intelligence"},{"name":"Qianqian Qiao","slug":"qianqian-qiao","given":"Qianqian","family":"Qiao","affiliation":"Beijing Electronic Science and Technology Institute"},{"name":"Yi Lu","slug":"yi-lu","given":"Yi","family":"Lu","affiliation":"Central Academy of Fine Arts"},{"name":"Huaye Wang","slug":"huaye-wang","given":"Huaye","family":"Wang","affiliation":"Beijing Electronic Science and Technology Institute"},{"name":"Shan Gao","slug":"shan-gao","given":"Shan","family":"Gao","affiliation":"Beijing University of Technology"},{"name":"Heng Huang","slug":"heng-huang","given":"Heng","family":"Huang","affiliation":"University of Science and Technology of China"},{"name":"Guangdong Li","slug":"guangdong-li","given":"Guangdong","family":"Li","affiliation":"Beijing Institute of Fashion Technology"}],"abstract":"Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relatively mature research environment. However, in contrast to the extensive research in photographic aesthetics, the field of aesthetic evaluation for paintings and drawings has seen limited attention until the introduction of the BAID dataset in March 2023. This dataset solely comprises overall scores for high-quality artistic images. \r\nOur research marks the pioneering introduction of a multi-attribute, multi-category dataset specifically tailored to the field of painting: Aesthetics of Paintings and Drawings Dataset (APDD). \r\nThe construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. This dataset encompasses 24 distinct artistic categories and 10 different aesthetic attributes. Each image in APDD has been evaluated by six professionally trained experts in the field of art, including assessments for both total aesthetic scores and aesthetic attribute scores. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries.\r\nConcurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. Through this research, our goal is to catalyze advancements in the field of aesthetic evaluation for paintings and drawings, while enriching the available resources and methodologies for its further development and application. Dataset is available at https://github.com/BestiVictory/APDD.git","doi":"10.24963/ijcai.2024/849","url":"https://ijcai.org/proceedings/2024/849","pdf":"https://ijcai.org/proceedings/2024/0849.pdf","markdown":"https://ijcai.org/proceedings/2024/849.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/849","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":849,"pages":{"first":7672,"last":7680},"keywords":["Theory and philosophy of arts and creativity in AI systems: Evaluation and curation of artistic or creative artefacts","Methods and resources: Datasets, knowledge bases and ontologies","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Application domains: Images, movies and visual arts"],"videos":[],"references":[{"position":1,"raw":"[Amirshahi et al., 2015] Seyed Ali Amirshahi, Gregor Uwe Hayn-Leichsenring, Joachim Denzler, and Christoph Redies. 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