Learning to Manipulate Artistic Images

• URL: http://arxiv.org/abs/2401.13976v1
• Date: Thu, 25 Jan 2024 06:34:49 GMT
• Title: Learning to Manipulate Artistic Images
• Authors: Wei Guo, Yuqi Zhang, De Ma, Qian Zheng
• Abstract summary: We propose an arbitrary Style Image Manipulation Network (SIM-Net) Our method balances computational efficiency and high resolution to a certain extent. Both qualitative and quantitative experiments demonstrate the superiority of our method over state-of-the-art methods.
• Score: 27.803374400458402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancement in computer vision has significantly lowered the barriers to artistic creation. Exemplar-based image translation methods have attracted much attention due to flexibility and controllability. However, these methods hold assumptions regarding semantics or require semantic information as the input, while accurate semantics is not easy to obtain in artistic images. Besides, these methods suffer from cross-domain artifacts due to training data prior and generate imprecise structure due to feature compression in the spatial domain. In this paper, we propose an arbitrary Style Image Manipulation Network (SIM-Net), which leverages semantic-free information as guidance and a region transportation strategy in a self-supervised manner for image generation. Our method balances computational efficiency and high resolution to a certain extent. Moreover, our method facilitates zero-shot style image manipulation. Both qualitative and quantitative experiments demonstrate the superiority of our method over state-of-the-art methods.Code is available at https://github.com/SnailForce/SIM-Net.

Related papers

• Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network [8.518945405991362]
  This paper explores the feasibility of using a deep learning network to localize manipulations in both shallow- and deep-fake images.<n>We propose a novel Guided Graph Neural Network (G-GNN) module that amplifies the distinction between manipulated and authentic pixels.
  arXiv  Detail & Related papers  (2026-01-05T21:38:50Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval [85.39613457282107]
  Cross-domain nature of sketch-based image retrieval is challenging. We present an effective Adapt and Align'' approach to address the key challenges. Inspired by recent advances in image-text foundation models (e.g., CLIP) on zero-shot scenarios, we explicitly align the learned image embedding with a more semantic text embedding to achieve the desired knowledge transfer from seen to unseen classes.
  arXiv  Detail & Related papers  (2023-05-09T03:10:15Z)
• HIRL: A General Framework for Hierarchical Image Representation Learning [54.12773508883117]
  We propose a general framework for Hierarchical Image Representation Learning (HIRL) This framework aims to learn multiple semantic representations for each image, and these representations are structured to encode image semantics from fine-grained to coarse-grained. Based on a probabilistic factorization, HIRL learns the most fine-grained semantics by an off-the-shelf image SSL approach and learns multiple coarse-grained semantics by a novel semantic path discrimination scheme.
  arXiv  Detail & Related papers  (2022-05-26T05:13:26Z)
• Neural Photometry-guided Visual Attribute Transfer [4.630419389180576]
  We present a deep learning-based method for propagating visual material attributes to larger samples of the same or similar materials. For training, we leverage images of the material taken under multiple illuminations and a dedicated data augmentation policy. Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain.
  arXiv  Detail & Related papers  (2021-12-05T09:22:28Z)
• 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)
• Grafit: Learning fine-grained image representations with coarse labels [114.17782143848315]
  This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
  arXiv  Detail & Related papers  (2020-11-25T19:06:26Z)
• Learning Visual Representations for Transfer Learning by Suppressing Texture [38.901410057407766]
  In self-supervised learning, texture as a low-level cue may provide shortcuts that prevent the network from learning higher level representations. We propose to use classic methods based on anisotropic diffusion to augment training using images with suppressed texture. We empirically show that our method achieves state-of-the-art results on object detection and image classification.
  arXiv  Detail & Related papers  (2020-11-03T18:27:03Z)
• Semantically Adaptive Image-to-image Translation for Domain Adaptation of Semantic Segmentation [1.8275108630751844]
  We address the problem of domain adaptation for semantic segmentation of street scenes. Many state-of-the-art approaches focus on translating the source image while imposing that the result should be semantically consistent with the input. We advocate that the image semantics can also be exploited to guide the translation algorithm.
  arXiv  Detail & Related papers  (2020-09-02T16:16:50Z)
• Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis [194.1452124186117]
  We propose a novel ECGAN for the challenging semantic image synthesis task. Our ECGAN achieves significantly better results than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-31T01:23:21Z)
• Learning Transformation-Aware Embeddings for Image Forensics [15.484408315588569]
  Image Provenance Analysis aims at discovering relationships among different manipulated image versions that share content. One of the main sub-problems for provenance analysis that has not yet been addressed directly is the edit ordering of images that share full content or are near-duplicates. This paper introduces a novel deep learning-based approach to provide a plausible ordering to images that have been generated from a single image through transformations.
  arXiv  Detail & Related papers  (2020-01-13T22:01:24Z)

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.