STint：地理空间数据的自监督时间插值
STint: Self-supervised Temporal Interpolation for Geospatial Data
AI总结：
针对地理空间数据时间插值依赖光流的问题，提出无监督自监督双循环一致性方法STint，无需光流或真值，实验表明显著优于现有方法。
摘要：
监督和无监督技术已展示了视频数据时间插值的潜力。然而，大多数主流时间插值技术依赖于光流，光流编码了视频帧之间像素的运动。另一方面，地理空间数据的时间分辨率较低，同时包含一系列运动和变形，这些挑战了光流固有的若干假设。在这项工作中，我们提出了一种无监督的时间插值技术，该技术不依赖地面真值数据，也不需要任何运动信息（如光流），从而为跨地理空间领域的更好泛化提供了一种有前景的替代方案。具体来说，我们引入了一种双循环一致性的自监督技术。我们提出的技术整合了多个循环一致性损失，这些损失源于通过一系列阶段在连续输入帧之间插值两帧。这种双循环一致性约束使模型以自监督方式生成中间帧。据我们所知，这是首次在没有显式使用光流的情况下尝试无监督时间插值。我们在多样化的地理空间数据集上的实验评估表明，STint在无监督时间插值方面显著优于现有的最先进方法。
英文摘要：
Supervised and unsupervised techniques have demonstrated the potential for temporal interpolation of video data. Nevertheless, most prevailing temporal interpolation techniques hinge on optical flow, which encodes the motion of pixels between video frames. On the other hand, geospatial data exhibits lower temporal resolution while encompassing a spectrum of movements and deformations that challenge several assumptions inherent to optical flow. In this work, we propose an unsupervised temporal interpolation technique, which does not rely on ground truth data or require any motion information like optical flow, thus offering a promising alternative for better generalization across geospatial domains. Specifically, we introduce a self-supervised technique of dual cycle consistency. Our proposed technique incorporates multiple cycle consistency losses, which result from interpolating two frames between consecutive input frames through a series of stages. This dual cycle consistent constraint causes the model to produce intermediate frames in a self-supervised manner. To the best of our knowledge, this is the first attempt at unsupervised temporal interpolation without the explicit use of optical flow. Our experimental evaluations across diverse geospatial datasets show that STint significantly outperforms existing state-of-the-art methods for unsupervised temporal interpolation.
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