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MAVFusion framework enhances infrared and visible video fusion efficiency

Researchers have developed MAVFusion, a novel framework for fusing infrared and visible videos efficiently. This method uses optical flow to identify dynamic regions, applying computationally intensive cross-modal attention only to these sparse areas. Static background regions are processed with a lighter interaction module, preserving temporal consistency and fine details while significantly speeding up inference. MAVFusion achieves state-of-the-art performance on video fusion benchmarks, reaching 14.16 FPS at 640x480 resolution. AI

IMPACT This method could improve real-time analysis in applications requiring fused infrared and visible video data.

RANK_REASON The item is a research paper detailing a new technical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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MAVFusion framework enhances infrared and visible video fusion efficiency

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xilai Li, Weijun Jiang, Xiaosong Li, Yang Liu, Hongbin Wang, Tao Ye, Huafeng Li, Haishu Tan ·

    MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction

    arXiv:2604.01958v2 Announce Type: replace Abstract: Infrared and visible video fusion combines the object saliency from infrared images with the texture details from visible images to produce semantically rich fusion results. However, most existing methods are designed for static…