Relevant bibliographies by topics / Transfert de style zero-shot

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

Academic literature on the topic 'Transfert de style zero-shot'

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Published: 25 May 2024

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Journal articles on the topic "Transfert de style zero-shot"

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Zhang, Yu, Rongjie Huang, Ruiqi Li, JinZheng He, Yan Xia, Feiyang Chen, Xinyu Duan, Baoxing Huai, and Zhou Zhao. "StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19597–605. http://dx.doi.org/10.1609/aaai.v38i17.29932.

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Style transfer for out-of-domain (OOD) singing voice synthesis (SVS) focuses on generating high-quality singing voices with unseen styles (such as timbre, emotion, pronunciation, and articulation skills) derived from reference singing voice samples. However, the endeavor to model the intricate nuances of singing voice styles is an arduous task, as singing voices possess a remarkable degree of expressiveness. Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase. To overcome these challenges, we propose StyleSinger, the first singing voice synthesis model for zero-shot style transfer of out-of-domain reference singing voice samples. StyleSinger incorporates two critical approaches for enhanced effectiveness: 1) the Residual Style Adaptor (RSA) which employs a residual quantization module to capture diverse style characteristics in singing voices, and 2) the Uncertainty Modeling Layer Normalization (UMLN) to perturb the style attributes within the content representation during the training phase and thus improve the model generalization. Our extensive evaluations in zero-shot style transfer undeniably establish that StyleSinger outperforms baseline models in both audio quality and similarity to the reference singing voice samples. Access to singing voice samples can be found at https://stylesinger.github.io/.
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Xi, Jier, Xiufen Ye, and Chuanlong Li. "Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target." Remote Sensing 14, no. 24 (December 10, 2022): 6260. http://dx.doi.org/10.3390/rs14246260.

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With the development of sonar technology, sonar images have been widely used to detect targets. However, there are many challenges for sonar images in terms of object detection. For example, the detectable targets in the sonar data are more sparse than those in optical images, the real underwater scanning experiment is complicated, and the sonar image styles produced by different types of sonar equipment due to their different characteristics are inconsistent, which makes it difficult to use them for sonar object detection and recognition algorithms. In order to solve these problems, we propose a novel sonar image object-detection method based on style learning and random noise with various shapes. Sonar style target sample images are generated through style transfer, which enhances insufficient sonar objects image. By introducing various noise shapes, which included points, lines, and rectangles, the problems of mud and sand obstruction and a mutilated target in the real environment are solved, and the single poses of the sonar image target is improved by fusing multiple poses of optical image target. In the meantime, a method of feature enhancement is proposed to solve the issue of missing key features when using style transfer on optical images directly. The experimental results show that our method achieves better precision.
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Wang, Wenjing, Jizheng Xu, Li Zhang, Yue Wang, and Jiaying Liu. "Consistent Video Style Transfer via Compound Regularization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12233–40. http://dx.doi.org/10.1609/aaai.v34i07.6905.

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Recently, neural style transfer has drawn many attentions and significant progresses have been made, especially for image style transfer. However, flexible and consistent style transfer for videos remains a challenging problem. Existing training strategies, either using a significant amount of video data with optical flows or introducing single-frame regularizers, have limited performance on real videos. In this paper, we propose a novel interpretation of temporal consistency, based on which we analyze the drawbacks of existing training strategies; and then derive a new compound regularization. Experimental results show that the proposed regularization can better balance the spatial and temporal performance, which supports our modeling. Combining with the new cost formula, we design a zero-shot video style transfer framework. Moreover, for better feature migration, we introduce a new module to dynamically adjust inter-channel distributions. Quantitative and qualitative results demonstrate the superiority of our method over other state-of-the-art style transfer methods. Our project is publicly available at: https://daooshee.github.io/CompoundVST/.
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Park, Jangkyoung, Ammar Ul Hassan, and Jaeyoung Choi. "CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)." Applied Sciences 12, no. 16 (August 10, 2022): 8005. http://dx.doi.org/10.3390/app12168005.

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Font generation using deep learning has made considerable progress using image style transfer, but the automatic conversion/generation of Chinese characters still remains a difficult task owing to the complex character shape and large number of Chinese characters. Most known Chinese character generation models use the image conversion method of the Chinese character shape itself; however, it is difficult to reproduce complex Chinese characters. Recent methods have utilized character compositionality by separating up to three or four components to improve the quality of generated characters, but it is still difficult to generate high-quality results for complex Chinese characters with many components. In this study, we proposed the CCFont model (component-based Chinese font generation model using generative adversarial networks (GANs)) that automatically generates all Chinese characters using Chinese character components (up to 17 components). The CCFont model generates all Chinese characters in various styles using the components of Chinese characters based on conditional GAN. By acquiring local style information from the components, the information is more accurate and there is less information loss than when global information is obtained from the image of the entire character, reducing the failure of style conversion and improving quality to produce high-quality results. Additionally, the CCFont model generates high-quality results without any additional training (zero-shot font generation without any additional training) for the first-seen characters and styles. For example, the CCFont model, which was trained with only traditional Chinese (TC) characters, generates high-quality results for languages that can be divided into components, such as Korean and Thai, as well as simplified Chinese (SC) characters that are only seen during inference. CCFont can be adopted as a multi-lingual font-generation model that can be applied to all languages, which can be divided into components. To the best of our knowledge, the proposed method is the first to generate a zero-shot multilingual generation model using components. Qualitative and quantitative experiments were conducted to demonstrate the effectiveness of the proposed method.
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Azizah, Kurniawati, and Wisnu Jatmiko. "Transfer Learning, Style Control, and Speaker Reconstruction Loss for Zero-Shot Multilingual Multi-Speaker Text-to-Speech on Low-Resource Languages." IEEE Access 10 (2022): 5895–911. http://dx.doi.org/10.1109/access.2022.3141200.

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Yang, Zhenhua, Dezhi Peng, Yuxin Kong, Yuyi Zhang, Cong Yao, and Lianwen Jin. "FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (March 24, 2024): 6603–11. http://dx.doi.org/10.1609/aaai.v38i7.28482.

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Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images. Although existing font generation methods have achieved satisfactory performance, they still struggle with complex characters and large style variations. To address these issues, we propose FontDiffuser, a diffusion-based image-to-image one-shot font generation method, which innovatively models the font imitation task as a noise-to-denoise paradigm. In our method, we introduce a Multi-scale Content Aggregation (MCA) block, which effectively combines global and local content cues across different scales, leading to enhanced preservation of intricate strokes of complex characters. Moreover, to better manage the large variations in style transfer, we propose a Style Contrastive Refinement (SCR) module, which is a novel structure for style representation learning. It utilizes a style extractor to disentangle styles from images, subsequently supervising the diffusion model via a meticulously designed style contrastive loss. Extensive experiments demonstrate FontDiffuser's state-of-the-art performance in generating diverse characters and styles. It consistently excels on complex characters and large style changes compared to previous methods. The code is available at https://github.com/yeungchenwa/FontDiffuser.
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Cheng, Jikang, Zhen Han, Zhongyuan Wang, and Liang Chen. "“One-Shot” Super-Resolution via Backward Style Transfer for Fast High-Resolution Style Transfer." IEEE Signal Processing Letters 28 (2021): 1485–89. http://dx.doi.org/10.1109/lsp.2021.3098230.

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Yu, Yong. "Few Shot POP Chinese Font Style Transfer using CycleGAN." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2171/1/012031.

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Abstract The new style design of Chinese fonts is an arduous task, because there are many types of commonly used Chinese characters and the composition of Chinese characters is complicated. Therefore, the style transfer of Chinese characters based on GAN has become a research hotspot in the past two years. This line of re-search is dedicated to using a small number of artificially designed new style fonts and learning the map-ping from the source font style domain to the target style domain. However, such methods have two problems: 1. The performance on pop (point of purchase) fonts with exaggerated and random style is not satisfying. 2. Plentiful manually designed fonts are still required. In order to solve the above problems, we propose a few-shot font style transfer model based on CycleGAN. It uses meta-knowledge to reduce the use of manually designed fonts and enables each character to fully learn the knowledge contained in all new style fonts to achieve satisfying pop font style transfer effect. We also construct a dataset based on commonly used 3500 Chinese characters and verify the effectiveness of our model.
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Zhu, Anna, Xiongbo Lu, Xiang Bai, Seiichi Uchida, Brian Kenji Iwana, and Shengwu Xiong. "Few-Shot Text Style Transfer via Deep Feature Similarity." IEEE Transactions on Image Processing 29 (2020): 6932–46. http://dx.doi.org/10.1109/tip.2020.2995062.

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Feng, Wancheng, Yingchao Liu, Jiaming Pei, Wenxuan Liu, Chunpeng Tian, and Lukun Wang. "Local Consistency Guidance: Personalized Stylization Method of Face Video (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23486–87. http://dx.doi.org/10.1609/aaai.v38i21.30440.

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Face video stylization aims to convert real face videos into specified reference styles. While one-shot methods perform well in single-image stylization, ensuring continuity between frames and retaining the original facial expressions present challenges in video stylization. To address these issues, our approach employs a personalized diffusion model with pixel-level control. We propose Local Consistency Guidance(LCG) strategy, composed of local-cross attention and local style transfer, to ensure temporal consistency. This framework enables the synthesis of high-quality stylized face videos with excellent temporal continuity.
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Dissertations / Theses on the topic "Transfert de style zero-shot"

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Fares, Mireille. "Multimodal Expressive Gesturing With Style." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS017.

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La génération de gestes expressifs permet aux agents conversationnels animés (ACA) d'articuler un discours d'une manière semblable à celle des humains. Le thème central du manuscrit est d'exploiter et contrôler l'expressivité comportementale des ACA en modélisant le comportement multimodal que les humains utilisent pendant la communication. Le but est (1) d’exploiter la prosodie de la parole, la prosodie visuelle et le langage dans le but de synthétiser des comportements expressifs pour les ACA; (2) de contrôler le style des gestes synthétisés de manière à pouvoir les générer avec le style de n'importe quel locuteur. Nous proposons un modèle de synthèse de gestes faciaux à partir du texte et la parole; et entraîné sur le corpus TEDx que nous avons collecté. Nous proposons ZS-MSTM 1.0, une approche permettant de synthétiser des gestes stylisés du haut du corps à partir du contenu du discours d'un locuteur source et correspondant au style de tout locuteur cible. Il est entraîné sur le corpus PATS qui inclut des données multimodales de locuteurs ayant des styles de comportement différents. Il n'est pas limité aux locuteurs de PATS, et génère des gestes dans le style de n'importe quel nouveau locuteur vu ou non par notre modèle, sans entraînement supplémentaire, ce qui rend notre approche «zero-shot». Le style comportemental est modélisé sur les données multimodales des locuteurs - langage, gestes et parole - et indépendamment de l'identité du locuteur. Nous proposons ZS-MSTM 2.0 pour générer des gestes faciaux stylisés en plus des gestes du haut du corps. Ce dernier est entraîné sur une extension de PATS, qui inclut des actes de dialogue et des repères faciaux en 2D
The generation of expressive gestures allows Embodied Conversational Agents (ECA) to articulate the speech intent and content in a human-like fashion. The central theme of the manuscript is to leverage and control the ECAs’ behavioral expressivity by modelling the complex multimodal behavior that humans employ during communication. The driving forces of the Thesis are twofold: (1) to exploit speech prosody, visual prosody and language with the aim of synthesizing expressive and human-like behaviors for ECAs; (2) to control the style of the synthesized gestures such that we can generate them with the style of any speaker. With these motivations in mind, we first propose a semantically aware and speech-driven facial and head gesture synthesis model trained on the TEDx Corpus which we collected. Then we propose ZS-MSTM 1.0, an approach to synthesize stylized upper-body gestures, driven by the content of a source speaker’s speech and corresponding to the style of any target speakers, seen or unseen by our model. It is trained on PATS Corpus which includes multimodal data of speakers having different behavioral style. ZS-MSTM 1.0 is not limited to PATS speakers, and can generate gestures in the style of any newly coming speaker without further training or fine-tuning, rendering our approach zero-shot. Behavioral style is modelled based on multimodal speakers’ data - language, body gestures, and speech - and independent from the speaker’s identity ("ID"). We additionally propose ZS-MSTM 2.0 to generate stylized facial gestures in addition to the upper-body gestures. We train ZS-MSTM 2.0 on PATS Corpus, which we extended to include dialog acts and 2D facial landmarks
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Lakew, Surafel Melaku. "Multilingual Neural Machine Translation for Low Resource Languages." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257906.

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Machine Translation (MT) is the task of mapping a source language to a target language. The recent introduction of neural MT (NMT) has shown promising results for high-resource language, however, poorly performing for low-resource language (LRL) settings. Furthermore, the vast majority of the 7, 000+ languages around the world do not have parallel data, creating a zero-resource language (ZRL) scenario. In this thesis, we present our approach to improving NMT for LRL and ZRL, leveraging a multilingual NMT modeling (M-NMT), an approach that allows building a single NMT to translate across multiple source and target languages. This thesis begins by i) analyzing the effectiveness of M-NMT for LRL and ZRL translation tasks, spanning two NMT modeling architectures (Recurrent and Transformer), ii) presents a self-learning approach for improving the zero-shot translation directions of ZRLs, iii) proposes a dynamic transfer-learning approach from a pre-trained (parent) model to a LRL (child) model by tailoring to the vocabulary entries of the latter, iv) extends M-NMT to translate from a source language to specific language varieties (e.g. dialects), and finally, v) proposes an approach that can control the verbosity of an NMT model output. Our experimental findings show the effectiveness of the proposed approaches in improving NMT of LRLs and ZRLs.
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Lakew, Surafel Melaku. "Multilingual Neural Machine Translation for Low Resource Languages." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257906.

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Machine Translation (MT) is the task of mapping a source language to a target language. The recent introduction of neural MT (NMT) has shown promising results for high-resource language, however, poorly performing for low-resource language (LRL) settings. Furthermore, the vast majority of the 7, 000+ languages around the world do not have parallel data, creating a zero-resource language (ZRL) scenario. In this thesis, we present our approach to improving NMT for LRL and ZRL, leveraging a multilingual NMT modeling (M-NMT), an approach that allows building a single NMT to translate across multiple source and target languages. This thesis begins by i) analyzing the effectiveness of M-NMT for LRL and ZRL translation tasks, spanning two NMT modeling architectures (Recurrent and Transformer), ii) presents a self-learning approach for improving the zero-shot translation directions of ZRLs, iii) proposes a dynamic transfer-learning approach from a pre-trained (parent) model to a LRL (child) model by tailoring to the vocabulary entries of the latter, iv) extends M-NMT to translate from a source language to specific language varieties (e.g. dialects), and finally, v) proposes an approach that can control the verbosity of an NMT model output. Our experimental findings show the effectiveness of the proposed approaches in improving NMT of LRLs and ZRLs.
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Book chapters on the topic "Transfert de style zero-shot"

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Huang, Yaoxiong, Mengchao He, Lianwen Jin, and Yongpan Wang. "RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering." In Computer Vision – ECCV 2020, 156–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58539-6_10.

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Xu, Ruiqi, Yongfeng Huang, Xin Chen, and Lin Zhang. "Specializing Small Language Models Towards Complex Style Transfer via Latent Attribute Pre-Training." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230591.

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In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios. Our dataset is the first large-scale data set of its kind, with 700 rephrased sentences and 1,000 sentences from the game Genshin Impact. While large language models (LLM) have shown promise in complex text style transfer, they have drawbacks such as data privacy concerns, network instability, and high deployment costs. To address these issues, we explore the effectiveness of small models (less than T5-3B) with implicit style pre-training through contrastive learning. We also propose a method for automated evaluation of text generation quality based on alignment with human evaluations using ChatGPT. Finally, we compare our approach with existing methods and show that our model achieves state-of-art performances of few-shot text style transfer models.
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Esmaeili Shayan, Mostafa. "Solar Energy and Its Purpose in Net-Zero Energy Building." In Zero-Energy Buildings - New Approaches and Technologies. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93500.

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The Net Zero Energy Building is generally described as an extremely energy-efficient building in which the residual electricity demand is provided by renewable energy. Solar power is also regarded to be the most readily available and usable form of renewable electricity produced at the building site. In contrast, energy conservation is viewed as an influential national for achieving a building’s net zero energy status. This chapter aims to show the value of the synergy between energy conservation and solar energy transfer to NZEBs at the global and regional levels. To achieve these goals, both energy demand building and the potential supply of solar energy in buildings have been forecasted in various regions, climatic conditions, and types of buildings. Building energy consumption was evaluated based on a bottom-up energy model developed by 3CSEP and data inputs from the Bottom-Up Energy Analysis System (BUENAS) model under two scenarios of differing degrees of energy efficiency intention. The study results indicate that the acquisition of sustainable energy consumption is critical for solar-powered net zero energy buildings in various building styles and environments. The chapter calls for the value of government measures that incorporate energy conservation and renewable energy.
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Das, Pranjit, P. S. Ramapraba, K. Seethalakshmi, M. Anitha Mary, S. Karthick, and Boopathi Sampath. "Sustainable Advanced Techniques for Enhancing the Image Process." In Fostering Cross-Industry Sustainability With Intelligent Technologies, 350–74. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-1638-2.ch022.

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This chapter discusses modern techniques for image improvement, including pixel editing, clarity enhancement, and minimal-size object recognition. An outline of photo enhancement and how deep learning could address its issues comes first. Both sophisticated techniques like cut-out and style transfer and frequently used ones like rotation and scaling are covered in this chapter. Additionally included are techniques for manipulating pixels, such as brightness adjustment, colour space conversion, and denoising algorithms. Assisting clarity issues like super-resolution, deblurring, and contrast amplification are also covered in this chapter. In order to address the issues with minimal-size object recognition, the chapter also looks into single-shot detectors and multi-scale networks. Through case studies and applications in medical imaging, autonomous driving, and surveillance systems, the value of these techniques is demonstrated. A discussion of prospective future study areas and how these techniques could affect computer vision and image processing brings the chapter to a close.
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Conference papers on the topic "Transfert de style zero-shot"

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Tang, Hao, Songhua Liu, Tianwei Lin, Shaoli Huang, Fu Li, Dongliang He, and Xinchao Wang. "Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01758.

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Lee, Sang-Hoon, Ha-Yeong Choi, Hyung-Seok Oh, and Seong-Whan Lee. "HierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer." In INTERSPEECH 2023. ISCA: ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-1608.

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Sun, Haochen, Lei Wu, Xiang Li, and Xiangxu Meng. "Style-woven Attention Network for Zero-shot Ink Wash Painting Style Transfer." In ICMR '22: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3512527.3531391.

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Izumi, Kota, and Keiji Yanai. "Zero-Shot Font Style Transfer with a Differentiable Renderer." In MMAsia '22: ACM Multimedia Asia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3551626.3564961.

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Liu, Kunhao, Fangneng Zhan, Yiwen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El Saddik, Shijian Lu, and Eric Xing. "StyleRF: Zero-Shot 3D Style Transfer of Neural Radiance Fields." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.00806.

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Fares, Mireille, Catherine Pelachaud, and Nicolas Obin. "Zero-Shot Style Transfer for Multimodal Data-Driven Gesture Synthesis." In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). IEEE, 2023. http://dx.doi.org/10.1109/fg57933.2023.10042658.

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Sheng, Lu, Ziyi Lin, Jing Shao, and Xiaogang Wang. "Avatar-Net: Multi-scale Zero-Shot Style Transfer by Feature Decoration." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00860.

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Yang, Serin, Hyunmin Hwang, and Jong Chul Ye. "Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.02091.

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Song, Kun, Yi Ren, Yi Lei, Chunfeng Wang, Kun Wei, Lei Xie, Xiang Yin, and Zejun Ma. "StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation." In INTERSPEECH 2023. ISCA: ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-648.

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Chen, Liyang, Zhiyong Wu, Runnan Li, Weihong Bao, Jun Ling, Xu Tan, and Sheng Zhao. "VAST: Vivify Your Talking Avatar via Zero-Shot Expressive Facial Style Transfer." In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2023. http://dx.doi.org/10.1109/iccvw60793.2023.00320.

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