[论文] AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Infere...

论文概要

研究领域: CV
作者: Hangfeng Liang, Yutao Hu, Yanhan Hu, Xiaohan Wu, Wenqi Shao, Ying Fu
发布时间: 2026-06-09
arXiv: 2606.11186

中文摘要

AnyMod-LLVE提出AMNet框架用于低光视频增强（LLVE），支持模态无关的灵活推理。引入空间-光谱双门控转换器学习辅助模态与RGB输入的对应关系，生成隐式辅助表征以支持鲁棒增强。基于RGB-only数据集和大规模多模态预训练，使模型能处理任意推理时模态组合，在模态缺失条件下表现优异。

原文摘要

Low-light video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent multimodal approaches have significantly improved enhancement performance by incorporating auxiliary modalities, such as event streams and infrared images. However, these methods typically assume the availability of these modalities at inference, which is often not feasible in real-world scenarios. To solve this problem, in this work, we propose AMNet, a unified multimodal framework for LLVE, to support flexible modality-agnostic inference, where auxiliary modalities may be unavailable. To address the issue of modality absence, we introduce a Spatial-Spectral Dual-Gated Translator that learns the correspondence between auxiliary modalities and RGB in...

自动采集于 2026-06-11

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