1 code implementation • 12 Feb 2024 • Xiaohao Xu, Tianyi Zhang, Sibo Wang, Xiang Li, Yongqi Chen, Ye Li, Bhiksha Raj, Matthew Johnson-Roberson, Xiaonan Huang
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
1 code implementation • 9 Jan 2024 • Sibo Wang, Jie Zhang, Zheng Yuan, Shiguang Shan
Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model.
1 code implementation • 4 Oct 2023 • Xiangyu Dong, Xingyi Zhang, Sibo Wang
Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs.
no code implementations • 3 Jun 2022 • Yanping Zheng, Hanzhi Wang, Zhewei Wei, Jiajun Liu, Sibo Wang
With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes.
3 code implementations • 9 Dec 2021 • Weijia Wu, Yuanqiang Cai, Debing Zhang, Sibo Wang, Zhuang Li, Jiahong Li, Yejun Tang, Hong Zhou
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
1 code implementation • 10 Jun 2021 • Xingyi Zhang, Kun Xie, Sibo Wang, Zengfeng Huang
Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes.
1 code implementation • 16 Jul 2020 • Yu Hao, Xin Cao, Yixiang Fang, Xike Xie, Sibo Wang
In attributed graphs, both the structure and attribute information can be utilized for link prediction.
1 code implementation • 9 Nov 2019 • Ke Xu, Kaiyu Guan, Jian Peng, Yunan Luo, Sibo Wang
The average accuracy is 93. 56%, compared with 85. 36% from CFMask.
2 code implementations • 5 Dec 2018 • Asad Khan, E. A. Huerta, Sibo Wang, Robert Gruendl, Elise Jennings, Huihuo Zheng
Furthermore, we use our neural network model as a feature extractor for unsupervised clustering and find that unlabeled DES images can be grouped together in two distinct galaxy classes based on their morphology, which provides a heuristic check that the learning is successfully transferred to the classification of unlabelled DES images.