Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations

• URL: http://arxiv.org/abs/2012.03377v1
• Date: Sun, 6 Dec 2020 21:05:23 GMT
• Title: Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations
• Authors: Akshay Joshi, Ankit Agrawal, Sushmita Nair
• Abstract summary: Artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision. In this paper, we investigate the use of deep self-supervised learning methods to solve the problem of recognizing complex artistic styles with high intra-class and low inter-class variation.
• Score: 5.835728107167379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision. Accurate categorization of paintings across different artistic movements and styles is critical for large-scale indexing of art databases. However, the automatic extraction and recognition of these highly dense artistic features has received little to no attention in the field of computer vision research. In this paper, we investigate the use of deep self-supervised learning methods to solve the problem of recognizing complex artistic styles with high intra-class and low inter-class variation. Further, we outperform existing approaches by almost 20% on a highly class imbalanced WikiArt dataset with 27 art categories. To achieve this, we train the EnAET semi-supervised learning model (Wang et al., 2019) with limited annotated data samples and supplement it with self-supervised representations learned from an ensemble of spatial and non-spatial transformations.

Related papers

• Deep Ensemble Art Style Recognition [2.3369294168789203]
  Huge digitization of artworks during the last decades created the need for categorization, analysis, and management of huge amounts of data related to abstract concepts. Recognition of various art features in artworks has gained attention in the deep learning society. In this paper, we are concerned with the problem of art style recognition using deep networks.
  arXiv  Detail & Related papers  (2024-05-19T21:26:11Z)
• Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models [47.19481598385283]
  ArtSavant is a tool to determine the unique style of an artist by comparing it to a reference dataset of works from WikiArt. We then perform a large-scale empirical study to provide quantitative insight on the prevalence of artistic style copying across 3 popular text-to-image generative models.
  arXiv  Detail & Related papers  (2024-04-11T17:59:43Z)
• AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art [36.21731898719347]
  We conduct a comprehensive analysis to position AI-generated art within the context of human art heritage. Our comparative analysis is based on an extensive dataset, dubbed ArtConstellation'' Key finding is that AI-generated artworks are visually related to the principle concepts for modern period art made in 1800-2000.
  arXiv  Detail & Related papers  (2024-02-04T11:49:51Z)
• CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion [74.44273919041912]
  Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. We build the innovative unified framework Creative Synth, which is based on a diffusion model with the ability to coordinate multimodal inputs.
  arXiv  Detail & Related papers  (2024-01-25T10:42:09Z)
• Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
  We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
  arXiv  Detail & Related papers  (2023-10-27T04:30:18Z)
• ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques [11.286457041998569]
  We show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model.
  arXiv  Detail & Related papers  (2023-08-29T13:15:13Z)
• Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models [64.24227572048075]
  We propose a Knowledge-Aware Prompt Tuning (KAPT) framework for vision-language models. Our approach takes inspiration from human intelligence in which external knowledge is usually incorporated into recognizing novel categories of objects.
  arXiv  Detail & Related papers  (2023-08-22T04:24:45Z)
• 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)
• ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer [60.6863849241972]
  We learn a representation of visual artistic style more strongly disentangled from the semantic content depicted in an image. We show that strongly addressing the disentanglement of style and content leads to large gains in style-specific metrics.
  arXiv  Detail & Related papers  (2023-04-12T10:33:18Z)
• AesUST: Towards Aesthetic-Enhanced Universal Style Transfer [15.078430702469886]
  AesUST is a novel Aesthetic-enhanced Universal Style Transfer approach. We introduce an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings. We also develop a new two-stage transfer training strategy with two aesthetic regularizations to train our model more effectively.
  arXiv  Detail & Related papers  (2022-08-27T13:51:11Z)
• Automatic analysis of artistic paintings using information-based measures [1.25456674968456]
  We identify hidden patterns and relationships present in artistic paintings by analysing their complexity. We apply Normalized Compression (NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings from 91 authors. We define a fingerprint that describes critical information regarding the artists' style, their artistic influences, and shared techniques.
  arXiv  Detail & Related papers  (2021-02-02T21:40:30Z)

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.