  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. Our 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). The 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. Concurrently,
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.
