no code implementations • 23 Apr 2024 • Buyun He, Yingguang Yang, Qi Wu, Hao liu, Renyu Yang, Hao Peng, Xiang Wang, Yong Liao, Pengyuan Zhou
To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network.
no code implementations • 4 Apr 2024 • Haoran Li, Haolin Shi, Wenli Zhang, Wenjun Wu, Yong Liao, Lin Wang, Lik-Hang Lee, Pengyuan Zhou
Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors.
no code implementations • 3 Feb 2024 • Long Ma, Jiajia Zhang, Hongping Deng, Ningyu Zhang, Yong Liao, Haiyang Yu
The escalating quality of video generated by advanced video generation methods leads to new security challenges in society, which makes generated video detection an urgent research priority.
no code implementations • 2 Jan 2024 • Qinglong Huang, Yong Liao, Yanbin Hao, Pengyuan Zhou
Neural radiance fields (NeRF) have been proposed as an innovative 3D representation method.
no code implementations • 26 Dec 2023 • Kun Lan, Haoran Li, Haolin Shi, Wenjun Wu, Yong Liao, Lin Wang, Pengyuan Zhou
Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration.
no code implementations • 17 Dec 2023 • Wei Tang, Zhiqian Wu, Yixin Cao, Yong Liao, Pengyuan Zhou
As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing.
no code implementations • 16 Dec 2023 • Xiaorui Jiang, Hengwei Xu, Yu Gao, Yong Liao, Pengyuan Zhou
Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data.
no code implementations • 18 Nov 2023 • Haoran Li, Long Ma, Yong Liao, Lechao Cheng, Yanbin Hao, Pengyuan Zhou
First, we segment the objects and the background in a multi-object image.
no code implementations • 13 Jun 2023 • Yan Shi, Yao Tian, Chengwei Tong, Chunyan Zhu, Qianqian Li, Mengzhu Zhang, Wei Zhao, Yong Liao, Pengyuan Zhou
Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears.
no code implementations • 4 Apr 2023 • Yao Lu, Pengyuan Zhou, Yong Liao, Haiyong Xie
Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance.
no code implementations • 17 Mar 2023 • Haoran Li, Pengyuan Zhou, Yihang Lin, Yanbin Hao, Haiyong Xie, Yong Liao
Video prediction is a complex time-series forecasting task with great potential in many use cases.
1 code implementation • 10 Mar 2023 • Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li, Tong Li, Yong Liao, Pengyuan Zhou
In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model.
no code implementations • 1 Mar 2023 • Yihang Lin, Pengyuan Zhou, Zhiqian Wu, Yong Liao
Federated learning allows clients to collaboratively train a global model without uploading raw data for privacy preservation.
1 code implementation • 16 Nov 2022 • Wei Tang, Benfeng Xu, Yuyue Zhao, Zhendong Mao, Yifeng Liu, Yong Liao, Haiyong Xie
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.
Ranked #1 on Relation Extraction on WebNLG
no code implementations • 30 Jul 2022 • Hengwei Xu, Yong Liao, Haiyong Xie, Pengyuan Zhou
The rapidly enlarging neural network models are becoming increasingly challenging to run on a single device.
no code implementations • 22 Feb 2022 • Pengyuan Zhou, Benjamin Finley, Lik-Hang Lee, Yong Liao, Haiyong Xie, Pan Hui
AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences.
1 code implementation • 16 Aug 2021 • Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.