Bibliografías temáticas / Transfert de style zero-shot

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Literatura académica sobre el tema "Transfert de style zero-shot"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Transfert de style zero-shot"

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Yao, Jixun, Yang Yuguang, Yu Pan, et al. "StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25669–77. https://doi.org/10.1609/aaai.v39i24.34758.

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Zero-shot voice conversion (VC) aims to transfer the timbre from the source speaker to an arbitrary unseen speaker while preserving the original linguistic content. Despite recent advancements in zero-shot VC using language model-based or diffusion-based approaches, several challenges remain: 1) current approaches primarily focus on adapting timbre from unseen speakers and are unable to transfer style and timbre to different unseen speakers independently; 2) these approaches often suffer from slower inference speeds due to the autoregressive modeling methods or the need for numerous sampling s
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Zhang, Yu, Rongjie Huang, Ruiqi Li, et al. "StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (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 disc
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Wang, Zhen, Zihang Lin, Meng Yuan, Yuehu Liu, and Chi Zhang. "Style Nursing with Spatial and Semantic Guidance for Zero-Shot Traffic Scene Style Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8214–22. https://doi.org/10.1609/aaai.v39i8.32886.

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Recent advances in text-to-image diffusion models have shown an outstanding ability in zero-shot style transfer. However, existing methods often struggle to balance preserving the semantic content of the input image and faithfully transferring the target style in line with the edit prompt. Especially when applied to complex traffic scenes with diverse objects, layouts, and stylistic variations, current diffusion models tend to exhibit Style Neglection, i.e., failing to generate the required style in the prompt. To address this issue, we propose Style Nursing, which directs the model to focus o
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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 (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 propos
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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 (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
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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 (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, bu
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An, Tianbo, Pingping Yan, Jiaai Zuo, Xing Jin, Mingliang Liu, and Jingrui Wang. "Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning." Electronics 13, no. 11 (2024): 2163. http://dx.doi.org/10.3390/electronics13112163.

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Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm det
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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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Güitta-López, Lucía, Lionel Güitta-López, Jaime Boal, and Álvaro J. López-López. "Sim-to-real transfer via a Style-Identified Cycle Consistent Generative Adversarial Network: Zero-shot deployment on robotic manipulators through visual domain adaptation." Engineering Applications of Artificial Intelligence 159 (November 2025): 111510. https://doi.org/10.1016/j.engappai.2025.111510.

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Cho, Kyusik, Dong Yeop Kim, and Euntai Kim. "Zero-Shot Scene Change Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2509–17. https://doi.org/10.1609/aaai.v39i3.32253.

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We present a novel, training-free approach to scene change detection. Our method leverages tracking models, which inherently perform change detection between consecutive frames of video by identifying common objects and detecting new or missing objects. Specifically, our method takes advantage of the change detection effect of the tracking model by inputting reference and query images instead of consecutive frames. Furthermore, we focus on the content gap and style gap between two input images in change detection, and address both issues by proposing adaptive content threshold and style bridgi
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Tesis sobre el tema "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
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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 mu
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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 mu
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Capítulos de libros sobre el tema "Transfert de style zero-shot"

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Li, Wendong, and Wei-Shi Zheng. "Text-Guided Zero-Shot 3D Style Transfer of Neural Radiance Fields." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-78186-5_9.

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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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58539-6_10.

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Liu, Guanghao, Yixin Zhong, Yuehui Chen, Yi Cao, and Yaou Zhao. "Stroke-Based Few-Shot Chinese Character Style Transfer." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5612-4_17.

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Chen, Zhiyong, Xinnuo Li, Zhiqi Ai, and Shugong Xu. "StyleFusion TTS: Multimodal Style-Control and Enhanced Feature Fusion for Zero-Shot Text-to-Speech Synthesis." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8795-1_18.

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Toshevska, Martina, and Sonja Gievska. "Large Language Models for Text Style Transfer: Exploratory Analysis of Prompting and Knowledge Augmentation Techniques." In Intelligent Environments 2024: Combined Proceedings of Workshops and Demos & Videos Session. IOS Press, 2024. http://dx.doi.org/10.3233/aise240025.

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Large language models have gained extensive research interest in the past few years. They have demonstrated remarkable ability to process and generate human-like text, and have improved performances on various natural language processing tasks. This paper is focused on the prompting techniques and knowledge augmentation techniques for text style transfer tasks. Text style transfer involves the transformation of a given sentence in a stylistically different manner while preserving its original meaning. It requires models to understand and manipulate different aspects such as politeness, formali
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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
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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
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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. 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 am
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Rajeshkumar, J., R. Chandrakala, A. Adaikkammai, et al. "Exploring Synergies and Innovations in GANs and Meta-Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7575-4.ch006.

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This survey paper aims at analyzing the two promising technologies in machine learning, that are GAN and Meta-Learning. GANs have brought the image synthesis and style transfer to new different level by using adversarial learning method while Meta-learning has done the same thing to few-shot learning and domain adaptation by helping the model to learn how it is going to learn. The paper starts with the theoretical background for GANs and Meta-Learning respectively and then delves into the application of GANs and its drawbacks, along with Meta-Learning. Enriched training methods like Wasserstei
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Actas de conferencias sobre el tema "Transfert de style zero-shot"

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Deng, Yingying, Xiangyu He, Fan Tang, and Weiming Dong. "Z*: Zero-shot Style Transfer via Attention Reweighting." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00662.

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Zhang, Yu, Ziyue Jiang, Ruiqi Li, et al. "TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.117.

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Yoon, Juhwan, Hyungseob Lim, Hyeonjin Cha, and Hong-Goo Kang. "StylebookTTS: Zero-Shot Text-to-Speech Leveraging Unsupervised Style Representation." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848787.

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Horvitz, Zachary, Ajay Patel, Kanishk Singh, Chris Callison-Burch, Kathleen McKeown, and Zhou Yu. "TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.781.

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Zhu, Xinfa, Lei He, Yujia Xiao, et al. "ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888535.

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Sun, Tao, Wenpeng Gao, and Fanbing Li. "Image Steganography Without Embedding Based on Style Transfer and Zero-Watermarking." In 2024 IEEE 4th International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2024. https://doi.org/10.1109/icdsca63855.2024.10860174.

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Zhou, Shuoyi, Yixuan Zhou, Weiqing Li., et al. "The Codec Language Model-Based Zero-Shot Spontaneous Style TTS System for CoVoC Challenge 2024." In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800681.

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Li, Yinghao Aaron, Xilin Jiang, Cong Han, and Nima Mesgarani. "StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.242.

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Lee, Hae Won, Ga Eun Park, Bo Min Kim, and Seong Geon Bae. "Improvement Research of Zero-DCE-Based Algorithm for Low-Light Style Transfer in Generative Cartoon GANs." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698147.

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Tang, Hao, Songhua Liu, Tianwei Lin, et al. "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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