1 code implementation • 10 Apr 2023 • Qihang Fang, Yafei Song, Keqiang Li, Li Shen, Huaiyu Wu, Gang Xiong, Liefeng Bo
Our key insight is that the better the geometry is, the lower-frequency the computed color field is.
1 code implementation • 23 Jul 2022 • Keqiang Li, Mingyang Zhao, Huaiyu Wu, Dong-Ming Yan, Zhen Shen, Fei-Yue Wang, Gang Xiong
We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds.
Ranked #3 on
Surface Normals Estimation
on PCPNet
no code implementations • 28 Apr 2022 • Sijia Li, Gaopeng Gou, Chang Liu, Chengshang Hou, Zhenzhen Li, Gang Xiong
In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum.
1 code implementation • 21 Apr 2022 • Tianyu Cui, Gaopeng Gou, Gang Xiong, Chang Liu, Peipei Fu, Zhen Li
6GAN forces multiple generators to train with a multi-class discriminator and an alias detector to generate non-aliased active targets with different addressing pattern types.
no code implementations • 20 Apr 2022 • Tianyu Cui, Gaopeng Gou, Gang Xiong
IPv6 scanning has always been a challenge for researchers in the field of network measurement.
1 code implementation • 20 Apr 2022 • Tianyu Cui, Gaopeng Gou, Gang Xiong, Zhen Li, Mingxin Cui, Chang Liu
To do this, we propose an IPv6 address correlation model - SiamHAN.
1 code implementation • 13 Feb 2022 • Xinjie Lin, Gang Xiong, Gaopeng Gou, Zhen Li, Junzheng Shi, Jing Yu
In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data.
no code implementations • 5 Aug 2020 • Tianyu Cui, Gang Xiong, Gaopeng Gou, Junzheng Shi, Wei Xia
Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power.
no code implementations • 6 Oct 2016 • Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.