StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

• URL: http://arxiv.org/abs/2103.15706v2
• Date: Wed, 31 Mar 2021 10:31:24 GMT
• Title: StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval
• Authors: Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song
• Abstract summary: Crossmodal matching problem is typically solved by learning a joint embedding space where semantic content shared between photo and sketch modalities are preserved. An effective model needs to explicitly account for this style diversity, crucially, to unseen user styles. Our model can not only disentangle the cross-modal shared semantic content, but can adapt the disentanglement to any unseen user style as well, making the model truly agnostic.
• Score: 119.03470556503942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn by humans and considerable style variations exist amongst different users. An effective SBIR model needs to explicitly account for this style diversity, crucially, to generalise to unseen user styles. To this end, a novel style-agnostic SBIR model is proposed. Different from existing models, a cross-modal variational autoencoder (VAE) is employed to explicitly disentangle each sketch into a semantic content part shared with the corresponding photo, and a style part unique to the sketcher. Importantly, to make our model dynamically adaptable to any unseen user styles, we propose to meta-train our cross-modal VAE by adding two style-adaptive components: a set of feature transformation layers to its encoder and a regulariser to the disentangled semantic content latent code. With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic. Extensive experiments show that our style-agnostic model yields state-of-the-art performance for both category-level and instance-level SBIR.

Related papers

• ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model [73.95608242322949]
  Stylized Text-to-Image Generation (STIG) aims to generate images from text prompts and style reference images. We present ArtWeaver, a novel framework that leverages pretrained Stable Diffusion to address challenges such as misinterpreted styles and inconsistent semantics.
  arXiv  Detail & Related papers  (2024-05-24T07:19:40Z)
• 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)
• MODIFY: Model-driven Face Stylization without Style Images [77.24793103549158]
  Existing face stylization methods always acquire the presence of the target (style) domain during the translation process. We propose a new method called MODel-drIven Face stYlization (MODIFY), which relies on the generative model to bypass the dependence of the target images. Experimental results on several different datasets validate the effectiveness of MODIFY for unsupervised face stylization.
  arXiv  Detail & Related papers  (2023-03-17T08:35:17Z)
• A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning [84.8813842101747]
  Unified Contrastive Arbitrary Style Transfer (UCAST) is a novel style representation learning and transfer framework. We present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components, i.e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
  arXiv  Detail & Related papers  (2023-03-09T04:35:00Z)
• Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation [120.96012935286913]
  We propose a novel adversarial style augmentation approach, which can generate hard stylized images during training. Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains.
  arXiv  Detail & Related papers  (2022-07-11T14:01:25Z)
• Towards Controllable and Photorealistic Region-wise Image Manipulation [11.601157452472714]
  We present a generative model with auto-encoder architecture for per-region style manipulation. We apply a code consistency loss to enforce an explicit disentanglement between content and style latent representations. The model is constrained by a content alignment loss to ensure the foreground editing will not interfere background contents.
  arXiv  Detail & Related papers  (2021-08-19T13:29:45Z)
• Anisotropic Stroke Control for Multiple Artists Style Transfer [36.92721585146738]
  Stroke Control Multi-Artist Style Transfer framework is developed. Anisotropic Stroke Module (ASM) endows the network with the ability of adaptive semantic-consistency among various styles. In contrast to the single-scale conditional discriminator, our discriminator is able to capture multi-scale texture clue.
  arXiv  Detail & Related papers  (2020-10-16T05:32:26Z)
• 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)

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.