3 citations · 6 across the 3 of their papers we have counts for
5 papers
Multimodal Robust Prompt Distillation for 3D Point Cloud Models
Xiang Gu, Liming Lu, Xu Zheng +3
Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense…
CIARD: Cyclic Iterative Adversarial Robustness Distillation
Liming Lu, Shuchao Pang, Xu Zheng +4
Adversarial robustness distillation (ARD) aims to transfer both performance and robustness from teacher model to lightweight student model, enabling resilient performance on resour…
Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Shuchao Pang, Zhenghan Chen, Shen Zhang +4
Deep neural networks for 3D point clouds have been demonstrated to be vulnerable to adversarial examples. Previous 3D adversarial attack methods often exploit certain information a…
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Shuchao Pang, Anan Du, Mehmet A. Orgun +5
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on Convolutional Neural Network…
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation
Shuchao Pang, Anan Du, Mehmet A. Orgun +5
Automatic tumor segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) h…