Personalized Image Enhancement Featuring Masked Style Modeling

• URL: http://arxiv.org/abs/2306.09334v1
• Date: Thu, 15 Jun 2023 17:59:02 GMT
• Title: Personalized Image Enhancement Featuring Masked Style Modeling
• Authors: Satoshi Kosugi, Toshihiko Yamasaki
• Abstract summary: We enhance input images for each user based on the user's preferred images. We propose a method named masked style modeling, which can predict a style for an input image considering the contents. We conduct quantitative evaluations and a user study, and our method trained using our training scheme successfully achieves content-aware personalization.
• Score: 42.400427631514596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address personalized image enhancement in this study, where we enhance input images for each user based on the user's preferred images. Previous methods apply the same preferred style to all input images (i.e., only one style for each user); in contrast to these methods, we aim to achieve content-aware personalization by applying different styles to each image considering the contents. For content-aware personalization, we make two contributions. First, we propose a method named masked style modeling, which can predict a style for an input image considering the contents by using the framework of masked language modeling. Second, to allow this model to consider the contents of images, we propose a novel training scheme where we download images from Flickr and create pseudo input and retouched image pairs using a degrading model. We conduct quantitative evaluations and a user study, and our method trained using our training scheme successfully achieves content-aware personalization; moreover, our method outperforms other previous methods in this field. Our source code is available at https://github.com/satoshi-kosugi/masked-style-modeling.

Related papers

• StyleBrush: Style Extraction and Transfer from a Single Image [19.652575295703485]
  Stylization for visual content aims to add specific style patterns at the pixel level while preserving the original structural features. We propose StyleBrush, a method that accurately captures styles from a reference image and brushes'' the extracted style onto other input visual content.
  arXiv  Detail & Related papers  (2024-08-18T14:27:20Z)
• Customizing Text-to-Image Models with a Single Image Pair [47.49970731632113]
  Art reinterpretation is the practice of creating a variation of a reference work, making a paired artwork that exhibits a distinct artistic style. We propose Pair Customization, a new customization method that learns stylistic difference from a single image pair and then applies the acquired style to the generation process.
  arXiv  Detail & Related papers  (2024-05-02T17:59:52Z)
• Measuring Style Similarity in Diffusion Models [118.22433042873136]
  We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image. We also propose a method to extract style attribute descriptors that can be used to style of a generated image to the images used in the training dataset of a text-to-image model.
  arXiv  Detail & Related papers  (2024-04-01T17:58:30Z)
• Customize StyleGAN with One Hand Sketch [0.0]
  We propose a framework to control StyleGAN imagery with a single user sketch. We learn a conditional distribution in the latent space of a pre-trained StyleGAN model via energy-based learning. Our model can generate multi-modal images semantically aligned with the input sketch.
  arXiv  Detail & Related papers  (2023-10-29T09:32:33Z)
• Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate [58.83278629019384]
  Style transfer aims to render the style of a given image for style reference to another given image for content reference. Existing approaches either apply the holistic style of the style image in a global manner, or migrate local colors and textures of the style image to the content counterparts in a pre-defined way. We propose Any-to-Any Style Transfer, which enables users to interactively select styles of regions in the style image and apply them to the prescribed content regions.
  arXiv  Detail & Related papers  (2023-04-19T15:15:36Z)
• Learning Diverse Tone Styles for Image Retouching [73.60013618215328]
  We propose to learn diverse image retouching with normalizing flow-based architectures. A joint-training pipeline is composed of a style encoder, a conditional RetouchNet, and the image tone style normalizing flow (TSFlow) module. Our proposed method performs favorably against state-of-the-art methods and is effective in generating diverse results.
  arXiv  Detail & Related papers  (2022-07-12T09:49:21Z)
• Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning [84.8813842101747]
  Contrastive Arbitrary Style Transfer (CAST) is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
  arXiv  Detail & Related papers  (2022-05-19T13:11:24Z)
• P$^2$-GAN: Efficient Style Transfer Using Single Style Image [2.703193151632043]
  Style transfer is a useful image synthesis technique that can re-render given image into another artistic style. Generative Adversarial Network (GAN) is a widely adopted framework toward this task for its better representation ability on local style patterns. In this paper, a novel Patch Permutation GAN (P$2$-GAN) network that can efficiently learn the stroke style from a single style image is proposed.
  arXiv  Detail & Related papers  (2020-01-21T12:08:08Z)

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.