Zobrazeno 1 - 10
of 3 813
pro vyhledávání: '"Wang, Yuping"'
Autor:
Anastassiou, Philip, Chen, Jiawei, Chen, Jitong, Chen, Yuanzhe, Chen, Zhuo, Chen, Ziyi, Cong, Jian, Deng, Lelai, Ding, Chuang, Gao, Lu, Gong, Mingqing, Huang, Peisong, Huang, Qingqing, Huang, Zhiying, Huo, Yuanyuan, Jia, Dongya, Li, Chumin, Li, Feiya, Li, Hui, Li, Jiaxin, Li, Xiaoyang, Li, Xingxing, Liu, Lin, Liu, Shouda, Liu, Sichao, Liu, Xudong, Liu, Yuchen, Liu, Zhengxi, Lu, Lu, Pan, Junjie, Wang, Xin, Wang, Yuping, Wang, Yuxuan, Wei, Zhen, Wu, Jian, Yao, Chao, Yang, Yifeng, Yi, Yuanhao, Zhang, Junteng, Zhang, Qidi, Zhang, Shuo, Zhang, Wenjie, Zhang, Yang, Zhao, Zilin, Zhong, Dejian, Zhuang, Xiaobin
We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech. Seed-TTS serves as a foundation model for speech generation and excels in sp
Externí odkaz:
http://arxiv.org/abs/2406.02430
This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, wh
Externí odkaz:
http://arxiv.org/abs/2405.06784
Autor:
Anastassiou, Philip, Tang, Zhenyu, Peng, Kainan, Jia, Dongya, Li, Jiaxin, Tu, Ming, Wang, Yuping, Wang, Yuxuan, Ma, Mingbo
We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre. Previous works have been cons
Externí odkaz:
http://arxiv.org/abs/2404.06674
The confluence of the advancement of Autonomous Vehicles (AVs) and the maturity of Vehicle-to-Everything (V2X) communication has enabled the capability of cooperative connected and automated vehicles (CAVs). Building on top of cooperative perception,
Externí odkaz:
http://arxiv.org/abs/2403.17916
Recent language model (LM) advancements have showcased impressive zero-shot voice conversion (VC) performance. However, existing LM-based VC models usually apply offline conversion from source semantics to acoustic features, demanding the complete so
Externí odkaz:
http://arxiv.org/abs/2401.11053
Autor:
Fuchs, Sebastian, Wang, Yuping
A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive information about
Externí odkaz:
http://arxiv.org/abs/2312.16544
Autor:
Liu, Xu, Zhou, Tong, Wang, Yuanxin, Wang, Yuping, Cao, Qinjingwen, Du, Weizhi, Yang, Yonghuan, He, Junjun, Qiao, Yu, Shen, Yiqing
The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities. Mirroring the transformative impact of found
Externí odkaz:
http://arxiv.org/abs/2312.10163
Autor:
Hetang, Congrui, Wang, Yuping
In this paper, we propose an approach for synthesizing novel view images from a single RGBD (Red Green Blue-Depth) input. Novel view synthesis (NVS) is an interesting computer vision task with extensive applications. Methods using multiple images has
Externí odkaz:
http://arxiv.org/abs/2311.01065
Autor:
Wang, Yuping, Chen, Jier
Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations. Traditional models often lack the sophistication needed to
Externí odkaz:
http://arxiv.org/abs/2310.17540
Autor:
Wang, Yuping, Chen, Jier
In autonomous driving, deep learning enabled motion prediction is a popular topic. A critical gap in traditional motion prediction methodologies lies in ensuring equivariance under Euclidean geometric transformations and maintaining invariant interac
Externí odkaz:
http://arxiv.org/abs/2310.13922