Perceptual Artifacts Localization for Image Synthesis Tasks

Abstract

Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, necessitating manual correction. In this study, we present a comprehensive empirical examination of Perceptual Artifacts Localization (PAL) spanning diverse image synthesis endeavors. We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels across ten synthesis tasks. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. Additionally, we illustrate its proficiency in adapting to previously unseen models using minimal training samples. We further propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images. Through our experimental analyses, we elucidate several invaluable downstream applications, such as automated artifact rectification, non-referential image quality evaluation, and abnormal region detection in images. The dataset and code are released here: https://owenzlz.github.io/PAL4VST

Cite

Text

Zhang et al. "Perceptual Artifacts Localization for Image Synthesis Tasks." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.00697

Markdown

[Zhang et al. "Perceptual Artifacts Localization for Image Synthesis Tasks." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/zhang2023iccv-perceptual/) doi:10.1109/ICCV51070.2023.00697

BibTeX

@inproceedings{zhang2023iccv-perceptual,
  title     = {{Perceptual Artifacts Localization for Image Synthesis Tasks}},
  author    = {Zhang, Lingzhi and Xu, Zhengjie and Barnes, Connelly and Zhou, Yuqian and Liu, Qing and Zhang, He and Amirghodsi, Sohrab and Lin, Zhe and Shechtman, Eli and Shi, Jianbo},
  booktitle = {International Conference on Computer Vision},
  year      = {2023},
  pages     = {7579-7590},
  doi       = {10.1109/ICCV51070.2023.00697},
  url       = {https://mlanthology.org/iccv/2023/zhang2023iccv-perceptual/}
}