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Reproducibility Companion Paper: Stable Diffusion for Content-Style Disentanglement in Art Analysis

Published: 07 June 2024 Publication History

Abstract

In this companion paper, we provide the artifacts of the GOYA model for disentangling content and style in art paintings, as presented at ICMR2023. The scripts are written in Python.

References

[1]
Xiao Liu, Spyridon Thermos, Gabriele Valvano, Agisilaos Chartsias, Alison O'Neil, and Sotirios A Tsaftaris. 2021. Measuring the Biases and Effectiveness of Content-Style Disentanglement. In BMVC.
[2]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In ICML.
[3]
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In CVPR.
[4]
Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, and Kiyoshi Tanaka. 2019. Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork. Transactions on Image Processing (2019).
[5]
Yankun Wu, Yuta Nakashima, and Noa Garcia. 2023. Not only generative art: Stable diffusion for content-style disentanglement in art analysis. In ICMR.

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  1. Reproducibility Companion Paper: Stable Diffusion for Content-Style Disentanglement in Art Analysis

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      Published In

      cover image ACM Conferences
      ICMR '24: Proceedings of the 2024 International Conference on Multimedia Retrieval
      May 2024
      1379 pages
      ISBN:9798400706196
      DOI:10.1145/3652583
      This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 June 2024

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      Author Tags

      1. art analysis
      2. representation disentanglement
      3. reproducibility
      4. text-to-image generation

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      • Short-paper

      Funding Sources

      • JST FOREST Grant
      • JSPS KAKENHI
      • JST CREST Grant

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      ICMR '24
      Sponsor:

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      Overall Acceptance Rate 254 of 830 submissions, 31%

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