Relevant bibliographies by topics / Multi-domain image translation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multi-domain image translation'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Multi-domain image translation"

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Liu, Yahui, Yajing Chem, Linchao Bao, Nicu Sebe, Bruno Lepri, and Nadai Marco De. "ISF-GAN: An Implicit Style Function for High Resolution Image-to-Image Translation." IEEE TRANSACTIONS ON MULTIMEDIA 25 (September 1, 2023): 3343–53. https://doi.org/10.1109/TMM.2022.3159115.

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Recently, there has been an increasing interest in image editing methods that employ pre-trained unconditional image generators (e.g., StyleGAN). However, applying these methods to translate images to multiple visual domains remains challenging. Existing works do not often preserve the domain-invariant part of the image (e.g., the identity in human face translations), or they do not usually handle multiple domains or allow for multi-modal translations. This work proposes an implicit style function (ISF) to straightforwardly achieve multi-modal and multi-domain image-to-image translation from p
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Shao, Mingwen, Youcai Zhang, Huan Liu, Chao Wang, Le Li, and Xun Shao. "DMDIT: Diverse multi-domain image-to-image translation." Knowledge-Based Systems 229 (October 2021): 107311. http://dx.doi.org/10.1016/j.knosys.2021.107311.

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Liu, Huajun, Lei Chen, Haigang Sui, Qing Zhu, Dian Lei, and Shubo Liu. "Unsupervised multi-domain image translation with domain representation learning." Signal Processing: Image Communication 99 (November 2021): 116452. http://dx.doi.org/10.1016/j.image.2021.116452.

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Xue, Teng, Tianchi Zhang, and Jing Zhang. "Research on Underwater Image Restoration Technology Based on Multi-Domain Translation." Journal of Marine Science and Engineering 11, no. 3 (2023): 674. http://dx.doi.org/10.3390/jmse11030674.

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Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles. Underwater image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. To address these challenges and further improve the quality of un
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Cai, Naxin, Houjin Chen, Yanfeng Li, Yahui Peng, and Linqiang Guo. "Registration on DCE-MRI images via multi-domain image-to-image translation." Computerized Medical Imaging and Graphics 104 (March 2023): 102169. http://dx.doi.org/10.1016/j.compmedimag.2022.102169.

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Xia, Weihao, Yujiu Yang, and Jing-Hao Xue. "Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement." Neural Networks 131 (November 2020): 50–63. http://dx.doi.org/10.1016/j.neunet.2020.07.023.

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Zhang, Yifei, Weipeng Li, Daling Wang, and Shi Feng. "Unsupervised Image Translation Using Multi-Scale Residual GAN." Mathematics 10, no. 22 (2022): 4347. http://dx.doi.org/10.3390/math10224347.

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Image translation is a classic problem of image processing and computer vision for transforming an image from one domain to another by learning the mapping between an input image and an output image. A novel Multi-scale Residual Generative Adversarial Network (MRGAN) based on unsupervised learning is proposed in this paper for transforming images between different domains using unpaired data. In the model, a dual generater architecture is used to eliminate the dependence on paired training samples and introduce a multi-scale layered residual network in generators for reducing semantic loss of
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Yang, Benchen, Xuzhao Liu, Yize Li, Haibo Jin, and Yetian Qu. "Multi-attention bidirectional contrastive learning method for unpaired image-to-image translation." PLOS ONE 19, no. 4 (2024): e0301580. http://dx.doi.org/10.1371/journal.pone.0301580.

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Unpaired image-to-image translation (I2IT) involves establishing an effective mapping between the source and target domains to enable cross-domain image transformation. Previous contrastive learning methods inadequately accounted for the variations in features between two domains and the interrelatedness of elements within the features. Consequently, this can result in challenges encompassing model instability and the blurring of image edge features. To this end, we propose a multi-attention bidirectional contrastive learning method for unpaired I2IT, referred to as MabCUT. We design separate
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Shen, Yangyun, Runnan Huang, and Wenkai Huang. "GD-StarGAN: Multi-domain image-to-image translation in garment design." PLOS ONE 15, no. 4 (2020): e0231719. http://dx.doi.org/10.1371/journal.pone.0231719.

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Cai, Youcheng, Runshi Li, and Ligang Liu. "MV2MV: Multi-View Image Translation via View-Consistent Diffusion Models." ACM Transactions on Graphics 43, no. 6 (2024): 1–12. http://dx.doi.org/10.1145/3687977.

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Image translation has various applications in computer graphics and computer vision, aiming to transfer images from one domain to another. Thanks to the excellent generation capability of diffusion models, recent single-view image translation methods achieve realistic results. However, directly applying diffusion models for multi-view image translation remains challenging for two major obstacles: the need for paired training data and the limited view consistency. To overcome the obstacles, we present a first unified multi-view image to multi-view image translation framework based on diffusion
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Dissertations / Theses on the topic "Multi-domain image translation"

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Liu, Yahui. "Exploring Multi-Domain and Multi-Modal Representations for Unsupervised Image-to-Image Translation." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342634.

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Unsupervised image-to-image translation (UNIT) is a challenging task in the image manipulation field, where input images in a visual domain are mapped into another domain with desired visual patterns (also called styles). An ideal direction in this field is to build a model that can map an input image in a domain to multiple target domains and generate diverse outputs in each target domain, which is termed as multi-domain and multi-modal unsupervised image-to-image translation (MMUIT). Recent studies have shown remarkable results in UNIT but they suffer from four main limitations: (1) State-of
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Mayet, Tsiry. "Multi-domain translation in a semi-supervised setting." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR46.

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Cette thèse explore la génération multi-modale dans un contexte d'apprentissage semi-supervisé, en abordant deux défis cruciaux : la prise en charge de configurations flexibles d'entrées et de sorties à travers plusieurs domaines, et le développement d'une stratégie d'entraînement efficace des données semi-supervisées. Alors que les systèmes d'intelligence artificielle progressent, il existe un besoin croissant de modèles capables d'intégrer et de générer de manière flexible plusieurs modalités, reflétant les capacités cognitives humaines. Les systèmes d'apprentissage profond conventionnels pe
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Wu, Po-Wui, and 吳柏威. "RA-GAN: Multi-domain Image-to-Image Translation via Relative Attributes." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6q5k8e.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>Multi-domain image-to-image translation has gained increasing attention recently. Previous methods take an image and some target attributes as inputs and generate an output image that has the desired attributes. However, this has one limitation. They require specifying the entire set of attributes even most of them would not be changed. To address this limitation, we propose RA-GAN, a novel and practical formulation to multi-domain image-to-image translation. The key idea is the use of relative attributes, which describes the desired change on selected attrib
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Hsu, Shu-Yu, and 許書宇. "SemiStarGAN: Semi-Supervised Generative Adversarial Networks for Multi-Domain Image-to-Image Translation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n4zqyy.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>Recent studies have shown significant advance for multi-domain image-to-image translation, and generative adversarial networks (GANs) are widely used to address this problem. However, existing methods all require a large number of domain-labeled images to train an effective image generator, but it may take time and effort to collect a large number of labeled data for real-world problems. In this thesis, we propose SemiStarGAN, a semi-supervised GAN network to tackle this issue. The proposed method utilizes unlabeled images by incorporating a novel discriminat
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Yung-YuChang and 張詠裕. "Multi-Domain Image-to-Image Translations based on Generative Adversarial Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/89654d.

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碩士<br>國立成功大學<br>工程科學系<br>106<br>In recent years, domain translation has been a breakthrough in the field of deep learning. However, most of the issues raised so far are dedicated to a single situation, and trained through paired datasets. The effect is significant, but the defect is that the architectures lack scalability and the paired data update in the future is difficult. The demand for computer vision assistance systems is increasing, and there is more than one mission requirement in some environments. In this Thesis, we propose a multi-domain image translation model which has two advanta
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Book chapters on the topic "Multi-domain image translation"

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He, Ziliang, Zhenguo Yang, Xudong Mao, Jianming Lv, Qing Li, and Wenyin Liu. "Self-attention StarGAN for Multi-domain Image-to-Image Translation." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30508-6_43.

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Cao, Jie, Huaibo Huang, Yi Li, Ran He, and Zhenan Sun. "Informative Sample Mining Network for Multi-domain Image-to-Image Translation." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58529-7_24.

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Pan, Bing, Zexuan Ji, and Qiang Chen. "MultiGAN: Multi-domain Image Translation from OCT to OCTA." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_28.

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Tang, Hao, Dan Xu, Wei Wang, Yan Yan, and Nicu Sebe. "Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_1.

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Hsu, Shu-Yu, Chih-Yuan Yang, Chi-Chia Huang, and Jane Yung-jen Hsu. "SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20870-7_21.

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Luo, Lei, and William H. Hsu. "AMMUNIT: An Attention-Based Multimodal Multi-domain UNsupervised Image-to-Image Translation Framework." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_30.

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Ge, Hongwei, Yao Yao, Zheng Chen, and Liang Sun. "Unsupervised Transformation Network Based on GANs for Target-Domain Oriented Multi-domain Image Translation." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20890-5_26.

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Choi, Changyong, Jiheon Jeong, Sangyoon Lee, Sang Min Lee, and Namkug Kim. "CT Kernel Conversion Using Multi-domain Image-to-Image Translation with Generator-Guided Contrastive Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43999-5_33.

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Xu, Caie, Jin Gan, Mingyang Wu, and Dandan Ni. "AU-GAN: Attention U-Net Based on a Built-In Attention for Multi-domain Image-to-Image Translation." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1354-1_18.

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Duong, Michael Tran, Sandhitsu R. Das, Pulkit Khandelwal, et al. "Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44858-4_1.

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Conference papers on the topic "Multi-domain image translation"

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Low, Spencer, Oliver Nina, Dylan Bowald, Angel D. Sappa, Nathan Inkawhich, and Peter Bruns. "Multi-modal Aerial View Image Challenge: Sensor Domain Translation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00315.

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Zhang, Lili. "Multi-Domain Image Translation Adversarial Network Based on Artificial Intelligence." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10968641.

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Lin, Jianxin, Yingce Xia, Yijun Wang, Tao Qin, and Zhibo Chen. "Image-to-Image Translation with Multi-Path Consistency Regularization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/413.

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Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which eval
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Zhang, Marc Yanlong, Zhiwu Huang, Danda Pani Paudel, Janine Thoma, and Luc Van Gool. "Weakly Paired Multi-Domain Image Translation." In British Machine Vision Conference 2020. British Machine Vision Association, 2020. https://doi.org/10.5244/c.34.178.

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Zhu, Yuanlue, Mengchao Bai, Linlin Shen, and Zhiwei Wen. "SwitchGAN for Multi-domain Facial Image Translation." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00209.

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Patil, Prashant W., Sunil Gupta, Santu Rana, Svetha Venkatesh, and Subrahmanyam Murala. "Multi-weather Image Restoration via Domain Translation." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01983.

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Gomez, Raul, Yahui Liu, Marco De Nadai, Dimosthenis Karatzas, Bruno Lepri, and Nicu Sebe. "Retrieval Guided Unsupervised Multi-domain Image to Image Translation." In MM '20: The 28th ACM International Conference on Multimedia. ACM, 2020. http://dx.doi.org/10.1145/3394171.3413785.

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Ge, Yingjun, Xiaodong Wang, and Jiting Zhou. "Federated learning based multi-domain image-to-image translation." In International Conference on Mechanisms and Robotics (ICMAR 2022), edited by Zeguang Pei. SPIE, 2022. http://dx.doi.org/10.1117/12.2652535.

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Hui, Le, Xiang Li, Jiaxin Chen, Hongliang He, and Jian Yang. "Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545169.

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Rahman, Mohammad Mahfujur, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan. "Multi-Component Image Translation for Deep Domain Generalization." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00067.

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