no code implementations • 16 Apr 2024 • Zhun Zhang, Yi Zeng, Qihe Liu, Shijie Zhou
In this paper, we seek to demystify this relationship by exploring the characteristics of adversarial perturbations within the frequency domain.
no code implementations • 10 Apr 2024 • Shijie Zhou, Zhiwen Fan, Dejia Xu, Haoran Chang, Pradyumna Chari, Tejas Bharadwaj, Suya You, Zhangyang Wang, Achuta Kadambi
This point cloud serves as the initial state for the centroids of 3D Gaussians.
no code implementations • 28 Feb 2024 • Zhewei Wu, Ruilong Yu, Qihe Liu, Shuying Cheng, Shilin Qiu, Shijie Zhou
Moreover, it can be seamlessly integrated with other visual trackers as a plug-and-play module without requiring any parameter adjustments.
no code implementations • 10 Feb 2024 • Rohan Sharma, Shijie Zhou, Kaiyi Ji, Changyou Chen
We consider the scenario of two networks, the attacker $\mathbf{A}$ and the trained defender $\mathbf{D}$ pitted against each other in an adversarial objective, wherein the attacker aims at teasing out the information of the data to be unlearned in order to infer membership, and the defender unlearns to defend the network against the attack, whilst preserving its general performance.
no code implementations • 6 Dec 2023 • Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Zehao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, Achuta Kadambi
In this work, we go one step further: in addition to radiance field rendering, we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation.
no code implementations • CVPR 2023 • Zhen Wang, Shijie Zhou, Jeong Joon Park, Despoina Paschalidou, Suya You, Gordon Wetzstein, Leonidas Guibas, Achuta Kadambi
One school of thought is to encode a latent vector for each point (point latents).
no code implementations • 3 Aug 2022 • Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, Suhang Wang
Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.
1 code implementation • 15 Oct 2021 • Enyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications.
Ranked #1 on Node Classification on Crocodile