English(EN) AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference

新AI框架可在无辅助数据情况下增强低光视频

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-09 17:59

研究人员开发了AMNet，一个新颖的多模态低光视频增强（LLVE）框架，即使在红外或事件流等辅助数据不可用时也能进行推理。该系统使用空间-光谱双门控翻译器从RGB输入生成隐式表示，从而实现鲁棒的增强。大量实验表明，AMNet在模态缺失条件下的性能优越，并且代码和模型已公开发布。 AI

影响该框架可以改善在挑战性光照条件下的视频分析和捕获，可能影响监控、自动驾驶和摄影。

排序理由该集群包含一篇详细介绍用于视频增强的新AI框架的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Hangfeng Liang, Yutao Hu, Yanhan Hu, Xiaohan Wu, Wenqi Shao, Ying Fu · 2026-06-10 04:00

AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference

arXiv:2606.11186v1 Announce Type: new Abstract: 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…
arXiv cs.CV TIER_1 English(EN) · Ying Fu · 2026-06-09 17:59

AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference

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…