arXiv cs.AI· Xiao Cao, Fuchen Long, Yansong Qu, Wen Xiao, Xin Zhang, Xinlong Sun, Xi Chen, Zhiyong Huang·· 1 天前AI 评分40
VideoEditor-MoE：用混合专家架构扩展复杂视频编辑
VideoEditor-MoE: Scaling Complex Video Editing with Mixture of Experts
AI 导读
VideoEditor-MoE 是首个开源的视频编辑混合专家（MoE）框架，除稀疏 MLP-MoE 层外还通过 MoE-Attention 以路由 key-value 专家扩展注意力容量，同时保留视频 token 间的全局交互。
正文 · 原文
Abstract:Complex video editing requires understanding complex instructions while jointly fulfilling multiple editing requests. Meeting these demands through dense model scaling substantially increases computational cost. We introduce VideoEditor-MoE, the first open-source mixture-of-experts framework for video editing. Beyond sparse MLP-MoE layers, MoE-Attention expands attention capacity through routed key--value experts while preserving global interactions among video tokens. Initialized from a 5B video editing model, VideoEditor-MoE scales to 14B and 56B parameters, with approximately 8B parameters activated per token in both variants. Comparisons with existing methods and two complementary baselines, Explicit-MoE and MLP-MoE, assess its benefits over explicit expert guidance and MLP-only expert architectures. Further analyses demonstrate that all experts remain active and reveal that learned routing responds more strongly to sample content and diffusion noise levels than to human-defined editing categories. Spatial analysis further indicates sensitivity to object-level semantic features. We also identify decoupled weight decay as a strategy for stabilizing expert scaling.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.03043 [cs.CV] |
| (or arXiv:2611.03043v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.03043 arXiv-issued DOI via DataCite |
Submission history
From: Xiao Cao [view email]
[v1]
Mon, 2 Nov 2026 12:42:51 UTC (6,654 KB)
[v2]
Mon, 9 Nov 2026 13:11:07 UTC (6,658 KB)
[v3]
Wed, 11 Nov 2026 11:48:20 UTC (6,655 KB)
来源：arXiv cs.AI · arxiv.org