Bibliografías temáticas / Zero-Shot Style Transfer

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Literatura académica sobre el tema "Zero-Shot Style Transfer"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Zero-Shot Style Transfer"

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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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2

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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4

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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5

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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6

Bai, Zhongyu, Hongli Xu, Qichuan Ding, and Xiangyue Zhang. "Side-Scan Sonar Image Classification with Zero-Shot and Style Transfer." IEEE Transactions on Instrumentation and Measurement, 2024, 1. http://dx.doi.org/10.1109/tim.2024.3352693.

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Fares, Mireille, Catherine Pelachaud, and Nicolas Obin. "Zero-shot style transfer for gesture animation driven by text and speech using adversarial disentanglement of multimodal style encoding." Frontiers in Artificial Intelligence 6 (June 12, 2023). http://dx.doi.org/10.3389/frai.2023.1142997.

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Modeling virtual agents with behavior style is one factor for personalizing human-agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different speakers including those unseen during training. Our model performs zero-shot multimodal style transfer driven by multimodal data from the PATS database containing videos of various speakers. We view style as being pervasive; while speaking, it colors the communicative behaviors expressivity while speech content is carried by multimodal signals an
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8

Xu, Hongli, Zhongyu Bai, Xiangyue Zhang, and Qichuan Ding. "MFSANet: Zero-Shot Side-Scan Sonar Image Recognition Based on Style Transfer." IEEE Geoscience and Remote Sensing Letters, 2023, 1. http://dx.doi.org/10.1109/lgrs.2023.3318051.

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9

Zhang, Qing, Jing Zhang, Xiangdong Su, Feilong Bao, and Guanglai Gao. "Contour detection network for zero-shot sketch-based image retrieval." Complex & Intelligent Systems, June 2, 2023. http://dx.doi.org/10.1007/s40747-023-01096-2.

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AbstractZero-shot sketch-based image retrieval (ZS-SBIR) is a challenging task that involves searching natural images related to a given hand-drawn sketch under the zero-shot scene. The previous approach projected image and sketch features into a low-dimensional common space for retrieval, and used semantic features to transfer the knowledge of seen to unseen classes. However, it is not effective enough to align multimodal features when projecting them into a common space, since the styles and contents of sketches and natural images are different and they are not one-to-one correspondence. To
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Tesis sobre el tema "Zero-Shot Style Transfer"

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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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2

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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3

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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Capítulos de libros sobre el tema "Zero-Shot Style Transfer"

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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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Actas de conferencias sobre el tema "Zero-Shot Style Transfer"

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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, 2023. http://dx.doi.org/10.21437/interspeech.2023-1608.

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2

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

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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. ACM, 2022. http://dx.doi.org/10.1145/3512527.3531391.

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Liu, Kunhao, Fangneng Zhan, Yiwen Chen, et al. "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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6

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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7

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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8

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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9

Song, Kun, Yi Ren, Yi Lei, et al. "StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation." In INTERSPEECH 2023. ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-648.

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10

Chen, Liyang, Zhiyong Wu, Runnan Li, et al. "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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