Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories

• URL: http://arxiv.org/abs/2405.02982v1
• Date: Sun, 5 May 2024 16:05:56 GMT
• Title: Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories
• Authors: Xin Jin, Qianqian Qiao, Yi Lu, Shan Gao, Heng Huang, Guangdong Li,
• Abstract summary: The Aesthetics of Paintings and Drawings dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries. The construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries.
• Score: 47.705077586687196
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

Related papers

• APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments [45.57709215036539]
  We introduce the Aesthetics Paintings and Drawings dataset (APDD), the first comprehensive collection of paintings encompassing 24 distinct artistic categories and 10 aesthetic attributes. APDDv2 boasts an expanded image corpus and improved annotation quality, featuring detailed language comments. We present an updated version of the Art Assessment Network for Specific Painting Styles, denoted as ArtCLIP. Experimental validation demonstrates the superior performance of this revised model in the realm of aesthetic evaluation, surpassing its predecessor in accuracy and efficacy.
  arXiv  Detail & Related papers  (2024-11-13T11:46:42Z)
• BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessment [7.7311099081131465]
  We explore the impact of local and global data augmentation techniques on artistic image aesthetic assessment (IAA) We introduce BackFlip, a local data augmentation technique designed specifically for artistic IAA. Our findings demonstrate that local augmentations, such as BackFlip, tend to outperform global augmentations on artistic IAA in most cases, probably because they do not perturb the composition of the art images.
  arXiv  Detail & Related papers  (2024-08-26T10:39:01Z)
• GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
  Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
  arXiv  Detail & Related papers  (2024-08-01T11:52:56Z)
• AACP: Aesthetics assessment of children's paintings based on self-supervised learning [17.672268781368672]
  The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA) Previous approaches have relied on training large datasets and providing an aesthetics score to the image. We construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning.
  arXiv  Detail & Related papers  (2024-03-12T12:07:00Z)
• Image Aesthetics Assessment via Learnable Queries [59.313054821874864]
  We propose the Image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.
  arXiv  Detail & Related papers  (2023-09-06T09:42:16Z)
• Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
  ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists. We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
  arXiv  Detail & Related papers  (2023-05-08T17:58:27Z)
• Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method [64.40494830113286]
  We first introduce a large-scale AIAA dataset: Boldbrush Artistic Image dataset (BAID), which consists of 60,337 artistic images covering various art forms. We then propose a new method, SAAN, which can effectively extract and utilize style-specific and generic aesthetic information to evaluate artistic images. Experiments demonstrate that our proposed approach outperforms existing IAA methods on the proposed BAID dataset.
  arXiv  Detail & Related papers  (2023-03-27T12:59:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.