DarkAct：面向多模态低光照动作识别的 RGB-热成像数据集与融合框架
DarkAct: A RGB-Thermal Dataset and Fusion Framework for Multimodal Low-Light Action Recognition
提供了低光照下配对的 RGB-热成像视频基准，并给出以人体为中心表征增强加自适应跨模态融合的 DarkAct-Net 框架，可直接迁移到夜间监控、机器人等需要跨模态融合的时序动作识别场景。
原文依据 We propose DarkAct-Net, an RGB-thermal fusion framework that enhances human-centric representation and achieves adaptive cross-modal fusion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论低光照环境下的人体动作识别（HAR）仍具挑战性，原因在于可见度下降、光照变化以及外观线索丢失。我们提出 DarkAct，一个大规模高质量 RGB-热成像视频数据集，专为低光照下的多模态动作识别而构建。DarkAct 包含 12,778 段配对的 RGB-热成像视频，覆盖 27 种人体动作，涉及多样视角与场景，为理解黑暗环境中的人体动作提供了新颖而全面的基准。我们在 DarkAct 上开展了大量实验，系统性地评测了单模态 HAR 模型、多模态融合框架以及视觉语言基础模型。它们在 DarkAct 上有限的性能凸显了在不利光照条件下对更鲁棒感知系统的迫切需求。为此，我们提出 DarkAct-Net，一个 RGB-热成像融合框架，增强以人体为中心的表征并实现自适应跨模态融合，从而在多样光照条件下实现鲁棒且细粒度的动作识别。数据集与代码见 https://github.com/darkact-creator/DarkAct.git。
英文原文摘要 1139 字符
Human action recognition (HAR) in low-light environments remains challenging due to degraded visibility, illumination variance, and loss of appearance cues. We introduce DarkAct, a large-scale and high-quality RGB-thermal video dataset purpose-built for multimodal action recognition under low illumination. DarkAct contains 12,778 paired RGB-thermal videos covering 27 human actions across diverse viewpoints and scenes, offering a novel and comprehensive benchmark for understanding human actions in dark environments. We conduct extensive experiments on DarkAct, systematically benchmarking unimodal HAR models, multimodal fusion frameworks, and vision-language foundation models. Their limited performance on DarkAct underscores the urgent need for more robust perception systems under adverse illumination. To address this, we propose DarkAct-Net, an RGB-thermal fusion framework that enhances human-centric representation and achieves adaptive cross-modal fusion, enabling robust and fine-grained action recognition across diverse lighting conditions. Dataset and code are available at https://github.com/darkact-creator/DarkAct.git.