Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Yin, Zixin"'
In this paper, we introduce a simple and novel framework for one-shot audio-driven talking head generation. Unlike prior works that require additional driving sources for controlled synthesis in a deterministic manner, we instead probabilistically sa
Externí odkaz:
http://arxiv.org/abs/2212.04248
We present a novel one-shot talking head synthesis method that achieves disentangled and fine-grained control over lip motion, eye gaze&blink, head pose, and emotional expression. We represent different motions via disentangled latent representations
Externí odkaz:
http://arxiv.org/abs/2211.14506
Recently, adversarial attacks for audio recognition have attracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the instance level to generate adversarial noises, which leads to expensive gen
Externí odkaz:
http://arxiv.org/abs/2211.10661
Publikováno v:
Industrial Robot: the international journal of robotics research and application, 2024, Vol. 51, Issue 1, pp. 177-188.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IR-09-2023-0207
While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples. Considering the safety critical applications of c-MARL,
Externí odkaz:
http://arxiv.org/abs/2204.07932
Autor:
Wang, Jiakai, Yin, Zixin, Hu, Pengfei, Liu, Aishan, Tao, Renshuai, Qin, Haotong, Liu, Xianglong, Tao, Dacheng
To operate in real-world high-stakes environments, deep learning systems have to endure noises that have been continuously thwarting their robustness. Data-end defense, which improves robustness by operations on input data instead of modifying models
Externí odkaz:
http://arxiv.org/abs/2204.06213
Autor:
Yin, Zixin1,2 (AUTHOR) yinzixin@ahszu.edu.cn, Jin, Zujin1,2 (AUTHOR) jinzj@ahszu.edu.cn, Li, Tongqing3 (AUTHOR) litongqing@cumt.edu.cn
Publikováno v:
Minerals (2075-163X). Jun2024, Vol. 14 Issue 6, p604. 22p.
Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive research attention in recent years. However, without exploiting the int
Externí odkaz:
http://arxiv.org/abs/2103.01050
Autor:
Feng, Xinyi, Yin, Zixin, Ou, Shiya, Chu, Zewen, Feng, Jun, Luo, Yuanyuan, Hu, Yaqi, Liu, Yanqing, Jiang, Wei, Wang, Xiaoqing, Wang, Haibo
Publikováno v:
In Journal of Ethnopharmacology 28 June 2023 310
Akademický článek
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