English(EN) Efficient Multi-modal Video Fusion Network with Dynamic Region-Guided Temporal Interaction

新型视频融合网络采用动态区域引导以提高效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一种高效的多模态视频融合网络，通过动态聚焦关键区域来提高时间连贯性和细节。该新方法避免了计算成本高昂的光流估计和特征扭曲，而是利用测试时训练范式来处理时间依赖性。通过仅在动态区域内建模时间交互并将预训练的MAE特征用于全局结构，该网络在保持高计算效率（适用于边缘设备）的同时，实现了卓越的融合质量和一致性。 AI

影响该方法通过降低计算负载，有望在边缘设备上实现更高效的视频处理。

排序理由该条目是一篇提交至arXiv的研究论文，详细介绍了一种新的技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jingxue Huang, Xiaosong Li, Yi Lin · 2026-10-27 04:00

具有动态区域引导时序交互的高效多模态视频融合网络

arXiv:2610.34765v1 Announce Type: new Abstract: Multi-modal video fusion aims to generate temporally coherent videos with salient targets and rich structural details by integrating complementary information from heterogeneous sensors. However, existing video fusion methods common…

本文如何被排名

报道来源 [1]

具有动态区域引导时序交互的高效多模态视频融合网络

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