摘要:随着视觉语言模型（VLM）、扩散模型（DM）、大语言模型（LLM）和音频基础模型（AFM）的日益普及，这些多模态基础模型可能会无意识地从其训练数据中编码敏感、受版权保护、有偏见或不安全的跨模态关联。在收到删除请求或政策更新后重新训练往往不切实际，而有针对性的遗忘依然困难，因为知识分布于共享的表示中。多模态遗忘通过支持跨模态的选择性移除，同时保持整体效用，来应对这一挑战。本综述提供了跨视觉、语言、音频和视频的多模态遗忘的统一、系统导向的视角，立足于最新进展、新兴应用和开放问题。我们的分类法使得能够跨模型架构和模态进行系统比较，澄清了删除强度、保留、效率、可逆性和鲁棒性之间的权衡。本综述强调了开放问题和实际考虑，以支持多模态遗忘的未来研究和部署。我们发布了一个精选仓库：this https URL

Abstract:With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data. Retraining after deletion requests or policy updates is often impractical, and targeted forgetting remains difficult because knowledge is distributed across shared representations. Multimodal unlearning addresses this challenge by enabling selective removal across modalities while retaining overall utility. This survey offers a unified, system-oriented view of multimodal unlearning across vision, language, audio, and video, grounded in recent advances, emerging applications, and open problems. Our taxonomy enables systematic comparison across model architectures and modalities, clarifying trade-offs among deletion strength, retention, efficiency, reversibility, and robustness. This survey highlights open problems and practical considerations to support future research and deployment of multimodal unlearning. We release a curated repository: this https URL