9 papers
Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning
Zhenyu Yu, Yangchen Zeng, Chunlei Meng +2
Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-level metrics. We challenge thes…
SteerFace: Debiasing Synthetic Face Generation via Adaptive Residue Perturbation
Yuxi Mi, Qiuyang Yuan, Jianqing Xu +5
The shortage of legally compliant data for face recognition training has sparked growing interest in using synthetic data as an alternative. While recent diffusion-based methods en…
Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration
Yi Liu, Jia Ma, Wengen Li +3
Recent advances in Image Restoration (IR) have been largely driven by generative methods such as Diffusion Models and Flow Matching, which excel in synthesizing realistic textures…
Towards Policy-Adaptive Image Guardrail: Benchmark and Method
Caiyong Piao, Zhiyuan Yan, Haoming Xu +4
Accurate rejection of sensitive or harmful visual content, i.e., harmful image guardrail, is critical in many application scenarios. This task must continuously adapt to the evolvi…
NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields
Xianliang Huang, Zhizhou Zhong, Shuhang Chen +3
Neural Radiance Fields (NeRF) have demonstrated remarkable performance in novel view synthesis. However, there is much improvement room on restoring 3D scenes based on NeRF from co…
Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion
Yuxi Mi, Zhizhou Zhong, Yuge Huang +7
Identity-preserving face synthesis aims to generate synthetic face images of virtual subjects that can substitute real-world data for training face recognition models. While prior…