Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization

• URL: http://arxiv.org/abs/2108.00449v1
• Date: Sun, 1 Aug 2021 12:54:46 GMT
• Title: Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization
• Authors: Dongkyu Lee, Zhiliang Tian, Lanqing Xue, Nevin L. Zhang
• Abstract summary: A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information. We propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content.
• Score: 15.444996697848266
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information. In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content. Furthermore, we fuse content information when building the target style representation, making it dynamic with respect to the content. Our method creates not only style-independent content representation, but also content-dependent style representation in transferring style. Empirical results show that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation. In addition, it is also competitive in terms of style transfer accuracy and fluency.

Related papers

• LLM-Enabled Style and Content Regularization for Personalized Text-to-Image Generation [14.508665960053643]
  The personalized text-to-image generation has rapidly advanced with the emergence of Stable Diffusion. Existing methods, which typically fine-tune models using embedded identifiers, often struggle with insufficient stylization and inaccurate image content. We propose style refinement and content preservation strategies.
  arXiv  Detail & Related papers  (2025-04-19T04:08:42Z)
• Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics [41.052284715017606]
  This paper presents a large meta-evaluation of metrics for evaluating style and attribute transfer.<n>We find that meta-evaluation studies on existing datasets lead to misleading conclusions about the suitability of metrics for content preservation.<n>We introduce a new test set specifically designed for evaluating content preservation metrics for style transfer.
  arXiv  Detail & Related papers  (2025-02-20T20:16:34Z)
• WikiStyle+: A Multimodal Approach to Content-Style Representation Disentanglement for Artistic Image Stylization [0.0]
  Artistic image stylization aims to render the content provided by text or image with the target style. Current methods for content and style disentanglement rely on image supervision. This paper proposes a multimodal approach to content-style disentanglement for artistic image stylization.
  arXiv  Detail & Related papers  (2024-12-19T03:42:58Z)
• DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer [13.588643982359413]
  Style transfer aims to fuse the artistic representation of a style image with the structural information of a content image. Existing methods train specific networks or utilize pre-trained models to learn content and style features. We propose a novel and training-free approach for style transfer, combining textual embedding with spatial features.
  arXiv  Detail & Related papers  (2024-10-19T06:42:43Z)
• StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples [48.44036251656947]
  Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings.
  arXiv  Detail & Related papers  (2024-10-16T17:25:25Z)
• Artist: Aesthetically Controllable Text-Driven Stylization without Training [19.5597806965592]
  We introduce textbfArtist, a training-free approach that aesthetically controls the content and style generation of a pretrained diffusion model for text-driven stylization. Our key insight is to disentangle the denoising of content and style into separate diffusion processes while sharing information between them. Our method excels at achieving aesthetic-level stylization requirements, preserving intricate details in the content image and aligning well with the style prompt.
  arXiv  Detail & Related papers  (2024-07-22T17:58:05Z)
• InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation [4.1177497612346]
  Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another. We introduce InstantStyle-Plus, an approach that prioritizes the integrity of the original content while seamlessly integrating the target style.
  arXiv  Detail & Related papers  (2024-06-30T18:05:33Z)
• Few-shot Image Generation via Style Adaptation and Content Preservation [60.08988307934977]
  We introduce an image translation module to GAN transferring, where the module teaches the generator to separate style and content. Our method consistently surpasses the state-of-the-art methods in few shot setting.
  arXiv  Detail & Related papers  (2023-11-30T01:16:53Z)
• InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer [22.29381866838179]
  Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. We propose a novel information disentanglement method, named InfoStyler, to capture the minimal sufficient information for both content and style representations.
  arXiv  Detail & Related papers  (2023-07-30T13:38:56Z)
• MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer [82.37710853235535]
  Unsupervised text style transfer task aims to rewrite a text into target style while preserving its main content. Traditional methods rely on the use of a fixed-sized vector to regulate text style, which is difficult to accurately convey the style strength for each individual token. Our proposed method addresses this issue by assigning individual style vector to each token in a text, allowing for fine-grained control and manipulation of the style strength.
  arXiv  Detail & Related papers  (2023-06-12T13:12:29Z)
• DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization [66.42741426640633]
  DiffStyler is a dual diffusion processing architecture to control the balance between the content and style of diffused results. We propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image.
  arXiv  Detail & Related papers  (2022-11-19T12:30:44Z)
• Arbitrary Style Transfer via Multi-Adaptation Network [109.6765099732799]
  A desired style transfer, given a content image and referenced style painting, would render the content image with the color tone and vivid stroke patterns of the style painting. A new disentanglement loss function enables our network to extract main style patterns and exact content structures to adapt to various input images.
  arXiv  Detail & Related papers  (2020-05-27T08:00:22Z)
• Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer [60.07283363509065]
  Unsupervised style transfer aims to change the style of an input sentence while preserving its original content. We propose a novel attentional sequence-to-sequence model that exploits the relevance of each output word to the target style. Experimental results show that our proposed model achieves state-of-the-art performance in terms of both transfer accuracy and content preservation.
  arXiv  Detail & Related papers  (2020-05-05T10:24:28Z)

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.