MMVIP：面向多天气海上视觉的可见光-红外配对数据集
MMVIP: A Visible-infrared Paired Dataset for Multi-weather Marine Vision
为可见光-红外跨模态研究提供了带精确时空对齐的海上多天气配对数据，可直接用于训练与评测图像配准、融合、跨模态翻译和海上目标检测模型，弥补了现有配对数据缺乏真实海上场景的空白。
原文依据 the first large-scale visible-infrared maritime vision dataset covering a wide spectrum of weather conditions and sea states
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论由于海洋天气与环境条件的复杂多变，海上多模态视觉面临显著挑战。尽管现代船舶普遍配备可见光与红外成像系统，但这两种模态的互补性从根本上依赖于准确的跨模态配准。然而，能够真实反映多样化海上场景的可见光-红外配对数据集的缺失，严重阻碍了该领域的进展。为克服这一局限，我们提出 MMVIP，这是首个覆盖广泛天气条件与海况的大规模可见光-红外海上视觉数据集。该数据集包含 128,100 张图像和 50 段视频序列，并具有精确的时空对齐。在图像配准、融合、海上目标检测与跨模态图像翻译任务上的全面评估证明了该数据集的有效性与挑战性。此外，MMVIP 为推进多模态海上感知建立了新的基准。数据集及相应基准已公开于：https://github.com/yyppptjr/MMVIP。
英文原文摘要 1142 字符
Maritime multimodal vision faces significant challenges due to the complexity and variability of oceanic weather and environmental conditions. While modern vessels are commonly equipped with visible and infrared imaging systems, the complementary nature of these modalities fundamentally depends on accurate cross-modal registration. However, the absence of paired visible-infrared datasets that realistically capture diverse maritime scenarios has severely hindered progress in this field. To overcome this limitation, we present MMVIP, the first large-scale visible-infrared maritime vision dataset covering a wide spectrum of weather conditions and sea states. The dataset contains 128,100 images and 50 video sequences with precise spatial-temporal alignment. Comprehensive evaluations across image registration, fusion, maritime object detection, and cross-modal image translation tasks demonstrate the dataset's effectiveness and challenge. Furthermore, MMVIP establishes a new benchmark for advancing multimodal maritime perception. The dataset and corresponding benchmarks are publicly available at: https://github.com/yyppptjr/MMVIP.