3 papers
cs.LG2026
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
Bo Wang, Jia Ni, Mengnan Zhao +2
The unauthorized use of personal data in model training has emerged as a growing privacy threat. Unlearnable examples (UEs) address this issue by embedding imperceptible perturbati…
cs.LG2025
BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models
Maozhen Zhang, Mengnan Zhao, Wei Wang +1
Prompt-based tuning has emerged as a lightweight alternative to full fine-tuning in large vision-language models, enabling efficient adaptation via learned contextual prompts. This…
cs.CV2023
EipFormer: Emphasizing Instance Positions in 3D Instance Segmentation
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
3D instance segmentation plays a crucial role in comprehending 3D scenes. Despite recent advancements in this field, existing approaches exhibit certain limitations. These methods…