Zobrazeno 1 - 10
of 430
pro vyhledávání: '"Zhu, Zihao"'
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. The emergence of foundation models as the "brain" of EAI agents for high-level task planning has shown promising results. However,
Externí odkaz:
http://arxiv.org/abs/2408.04449
Autor:
Wu, Baoyuan, Chen, Hongrui, Zhang, Mingda, Zhu, Zihao, Wei, Shaokui, Yuan, Danni, Zhu, Mingli, Wang, Ruotong, Liu, Li, Shen, Chao
As an emerging approach to explore the vulnerability of deep neural networks (DNNs), backdoor learning has attracted increasing interest in recent years, and many seminal backdoor attack and defense algorithms are being developed successively or conc
Externí odkaz:
http://arxiv.org/abs/2407.19845
LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion
Autor:
Zhu, Zihao, Tao, Tianli, Tao, Yitian, Deng, Haowen, Cai, Xinyi, Wu, Gaofeng, Wang, Kaidong, Tang, Haifeng, Zhu, Lixuan, Gu, Zhuoyang, Huang, Jiawei, Shen, Dinggang, Zhang, Han
The infant brain undergoes rapid development in the first few years after birth.Compared to cross-sectional studies, longitudinal studies can depict the trajectories of infants brain development with higher accuracy, statistical power and flexibility
Externí odkaz:
http://arxiv.org/abs/2405.10691
In the film and gaming industries, achieving a realistic hair appearance typically involves the use of strands originating from the scalp. However, reconstructing these strands from observed surface images of hair presents significant challenges. The
Externí odkaz:
http://arxiv.org/abs/2403.17496
Autor:
Guo, Lianghu, Tao, Tianli, Cai, Xinyi, Zhu, Zihao, Huang, Jiawei, Zhu, Lixuan, Gu, Zhuoyang, Tang, Haifeng, Zhou, Rui, Han, Siyan, Liang, Yan, Yang, Qing, Shen, Dinggang, Zhang, Han
Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the
Externí odkaz:
http://arxiv.org/abs/2402.13776
Few-shot class-incremental learning (FSCIL) aims to continually fit new classes with limited training data, while maintaining the performance of previously learned classes. The main challenges are overfitting the rare new training samples and forgett
Externí odkaz:
http://arxiv.org/abs/2401.07208
Autor:
Chen, Kaiwen, Zhu, Zihao, Xie, Yaofeng, Hillier, Adrian D., Lord, James S., Dai, Pengcheng, Shu, Lei
The recently discovered (Ba,Sr)Ni$_2$As$_2$ family provides an ideal platform for investigating the interaction between electronic nematicity and superconductivity. Here we report the muon spin relaxation ($\mu$SR) measurements on BaNi$_2$As$_2$. Tra
Externí odkaz:
http://arxiv.org/abs/2401.04546
Autor:
Liu, Haiyang, Zhu, Zihao, Becherini, Giorgio, Peng, Yichen, Su, Mingyang, Zhou, You, Zhe, Xuefei, Iwamoto, Naoya, Zheng, Bo, Black, Michael J.
We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new mesh-level holistic c
Externí odkaz:
http://arxiv.org/abs/2401.00374
Adversarial examples are well-known tools to evaluate the vulnerability of deep neural networks (DNNs). Although lots of adversarial attack algorithms have been developed, it is still challenging in the practical scenario that the model's parameters
Externí odkaz:
http://arxiv.org/abs/2312.16979
Autor:
Wu, Baoyuan, Wei, Shaokui, Zhu, Mingli, Zheng, Meixi, Zhu, Zihao, Zhang, Mingda, Chen, Hongrui, Yuan, Danni, Liu, Li, Liu, Qingshan
Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular case
Externí odkaz:
http://arxiv.org/abs/2312.08890