Zobrazeno 1 - 10
of 228
pro vyhledávání: '"LIU Yunfan"'
Publikováno v:
Cailiao gongcheng, Vol 51, Iss 11, Pp 61-70 (2023)
The static and dynamic corrosion experimental platforms of supercritical fluid reactor were used for the static and dynamic corrosion test of Q235, 304, 316L, P91, N80 and 3Cr13 in the supercritical water pseudo-critical region (22.5 MPa, 375.6 ℃)
Externí odkaz:
https://doaj.org/article/cb81c30155a64be6a14b3df2f10acb77
Autor:
Yu, Hongtian, Li, Yangu, Wu, Mingrui, Shen, Letian, Liu, Yue, Song, Yunxuan, Ye, Qixiang, Lyu, Xiaorui, Mao, Yajun, Zheng, Yangheng, Liu, Yunfan
In high-energy physics, anti-neutrons ($\bar{n}$) are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties provides an important probe for understanding the governing principles.
Externí odkaz:
http://arxiv.org/abs/2408.10599
To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge from a well-trained teacher GNN into a student MLP. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2407.14768
To mitigate the computational complexity in the self-attention mechanism on long sequences, linear attention utilizes computation tricks to achieve linear complexity, while state space models (SSMs) popularize a favorable practice of using non-data-d
Externí odkaz:
http://arxiv.org/abs/2406.08128
A fundamental problem in learning robust and expressive visual representations lies in efficiently estimating the spatial relationships of visual semantics throughout the entire image. In this study, we propose vHeat, a novel vision backbone model th
Externí odkaz:
http://arxiv.org/abs/2405.16555
Autor:
Liu, Yue, Tian, Yunjie, Zhao, Yuzhong, Yu, Hongtian, Xie, Lingxi, Wang, Yaowei, Ye, Qixiang, Liu, Yunfan
Designing computationally efficient network architectures persists as an ongoing necessity in computer vision. In this paper, we transplant Mamba, a state-space language model, into VMamba, a vision backbone that works in linear time complexity. At t
Externí odkaz:
http://arxiv.org/abs/2401.10166
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this limitation may be inherently attributed to the lack of spatial invari
Externí odkaz:
http://arxiv.org/abs/2308.10561
The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification. A common practice for solving this problem is to modify the original data so that it could be pro
Externí odkaz:
http://arxiv.org/abs/2306.14640
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of driving subjects would inevitably interfere with the re-enactment resu
Externí odkaz:
http://arxiv.org/abs/2211.12674
Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensi
Externí odkaz:
http://arxiv.org/abs/2210.12683