Beyond Sample-Level Forgetting: Improving Reliability in Multimodal Unlearning

摘要

Multimodal unlearning seeks to remove specific data from pretrained multimodal models while preserving reliability and locality. This work uses decoupled knowledge components, Multimodal Variational Inference, and contrastive semantic editing to improve refined forgetting under privacy- and copyright-sensitive scenarios.

汪建洲

23级学硕

巫义锐

青年教授, CCF 高级会员

My research interests include Computer Vision, Artifical Intelligence, Multimedia Computing and Intelligent Water Conservancy.

袁俐新

讲师

张文逍

助理研究员