no code implementations • 17 Oct 2024 • Kangkang Lu, Yanhua Yu, Zhiyong Huang, Jia Li, Yuling Wang, Meiyu Liang, Xiting Qin, Yimeng Ren, Tat-Seng Chua, Xidian Wang
Specifically, we propose a Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which employs a dual-module approach: local independent filtering and global hybrid filtering.
no code implementations • 17 Aug 2024 • Xiao Cao, Beibei Lin, Bo wang, Zhiyong Huang, Robby T. Tan
To address these artifacts and enhance robustness, we propose SSNeRF, a sparse view semi supervised NeRF method based on a teacher student framework.
no code implementations • 22 Apr 2024 • Yihang Wu, Xiao Cao, Kaixin Li, Zitan Chen, Haonan Wang, Lei Meng, Zhiyong Huang
To achieve this, we incorporate a temperature control mechanism within the early phases of the self-attention modules to mitigate entity leakage issues.
3 code implementations • 15 Apr 2024 • Kaixin Li, Yuchen Tian, Qisheng Hu, Ziyang Luo, Zhiyong Huang, Jing Ma
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts.
no code implementations • 6 Mar 2024 • Yifan Bao, Yihao Ang, Qiang Huang, Anthony K. H. Tung, Zhiyong Huang
This underscores its adeptness in seamlessly integrating latent features with external conditions.
no code implementations • 28 Dec 2023 • Yajing Zhai, Yawen Zeng, Zhiyong Huang, Zheng Qin, Xin Jin, Da Cao
Thereby, this paper explores the potential of using the generated multiple person attributes as prompts in ReID tasks with off-the-shelf (large) models for more accurate retrieval results.
1 code implementation • 7 Sep 2023 • Yihao Ang, Qiang Huang, Yifan Bao, Anthony K. H. Tung, Zhiyong Huang
Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation.
no code implementations • 4 Dec 2020 • Zhiyong Huang, Kekai Sheng, WeiMing Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Dengwen Zhou, Changsheng Xu
For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain.