Masked and Adaptive Transformer for Exemplar Based Image Translation

• URL: http://arxiv.org/abs/2303.17123v1
• Date: Thu, 30 Mar 2023 03:21:14 GMT
• Title: Masked and Adaptive Transformer for Exemplar Based Image Translation
• Authors: Chang Jiang, Fei Gao, Biao Ma, Yuhao Lin, Nannan Wang, Gang Xu
• Abstract summary: Cross-domain semantic matching is challenging. We propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence. We devise a novel contrastive style learning method, for acquire quality-discriminative style representations.
• Score: 16.93344592811513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at \url{https://github.com/AiArt-HDU/MATEBIT}.

Related papers

• PixelShuffler: A Simple Image Translation Through Pixel Rearrangement [0.0]
  Style transfer is a widely researched application of image-to-image translation, where the goal is to synthesize an image that combines the content of one image with the style of another. Existing state-of-the-art methods often rely on complex neural networks, including diffusion models and language models, to achieve high-quality style transfer. We propose a novel pixel shuffle method that addresses the image-to-image translation problem generally with a specific demonstrative application in style transfer.
  arXiv  Detail & Related papers  (2024-10-03T22:08:41Z)
• SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation [18.93434486338439]
  SCONE-GAN is shown to be effective for learning to generate realistic and diverse scenery images. For more realistic and diverse image generation we introduce style reference image. We validate the proposed algorithm for image-to-image translation and stylizing outdoor images.
  arXiv  Detail & Related papers  (2023-11-07T10:29:16Z)
• Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance [51.188396199083336]
  We present an approach that guides the reverse process of diffusion sampling by applying asymmetric gradient guidance. Our model's adaptability allows it to be implemented with both image-fusion and latent-dif models. Experiments show that our method outperforms various state-of-the-art models in image translation tasks.
  arXiv  Detail & Related papers  (2023-06-07T12:56:56Z)
• Conditional Score Guidance for Text-Driven Image-to-Image Translation [52.73564644268749]
  We present a novel algorithm for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our method aims to generate a target image by selectively editing the regions of interest in a source image.
  arXiv  Detail & Related papers  (2023-05-29T10:48:34Z)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)
• Unsupervised Image-to-Image Translation with Generative Prior [103.54337984566877]
  Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. We present a novel framework, Generative Prior-guided UN Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm.
  arXiv  Detail & Related papers  (2022-04-07T17:59:23Z)
• Global and Local Alignment Networks for Unpaired Image-to-Image Translation [170.08142745705575]
  The goal of unpaired image-to-image translation is to produce an output image reflecting the target domain's style. Due to the lack of attention to the content change in existing methods, semantic information from source images suffers from degradation during translation. We introduce a novel approach, Global and Local Alignment Networks (GLA-Net) Our method effectively generates sharper and more realistic images than existing approaches.
  arXiv  Detail & Related papers  (2021-11-19T18:01:54Z)
• Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization [72.65828901909708]
  Controllable person image generation aims to produce realistic human images with desirable attributes. We introduce a novel Spatially-Adaptive Warped Normalization (SAWN), which integrates a learned flow-field to warp modulation parameters. We propose a novel self-training part replacement strategy to refine the pretrained model for the texture-transfer task.
  arXiv  Detail & Related papers  (2021-05-31T07:07:44Z)
• Cross-domain Correspondence Learning for Exemplar-based Image Translation [59.35767271091425]
  We present a framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We show that our method is superior to state-of-the-art methods in terms of image quality significantly.
  arXiv  Detail & Related papers  (2020-04-12T09:10:57Z)

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.