通过时间解耦专家与分布驱动对比正则化的半监督视频去雪网络
Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization
AI总结：
针对真实雪视频去雪缺乏成对训练数据的问题，提出半监督视频去雪网络SemiVDN，通过分布驱动对比正则化缩小合成与真实数据分布差距，并利用先验引导时间解耦专家模块分解物理分量，在基准和真实数据上优于现有方法。
摘要：
雪退化通过在室外场景中造成不良损坏，对计算机视觉任务的推进提出了严峻挑战。尽管当前基于深度学习的去雪方法在合成基准数据集上取得了成功，但由于缺乏成对的真实世界训练数据，它们难以恢复分布外的真实世界雪视频。为解决这一瓶颈，我们设计了一种半监督精神的视频去雪新范式，引入未标记的真实数据以实现可泛化的去雪。具体而言，我们构建了一个包含85个雪视频的真实世界数据集，随后提出了一种半监督视频去雪网络（SemiVDN），配备了新颖的分布驱动对比正则化。精心设计的对比正则化缓解了合成数据与真实数据间的分布差距，从而维持了所需的雪不变背景细节。此外，基于大气散射模型，我们引入了先验引导时间解耦专家模块，以帧相关方式分解构成雪视频的物理分量。我们在基准数据集和收集的真实雪数据上评估了SemiVDN。实验结果证明了该方法优于最先进的图像级和视频级去雪方法。
英文摘要：
Snow degradations present formidable challenges to the advancement of computer vision tasks by the undesirable corruption in outdoor scenarios. While current deep learning-based desnowing approaches achieve success on synthetic benchmark datasets, they struggle to restore out-of-distribution real-world snowy videos due to the deficiency of paired real-world training data. To address this bottleneck, we devise a new paradigm for video desnowing in a semi-supervised spirit to involve unlabeled real data for the generalizable snow removal. Specifically, we construct a real-world dataset with 85 snowy videos, and then present a Semi-supervised Video Desnowing Network (SemiVDN) equipped by a novel Distribution-driven Contrastive Regularization. The elaborated contrastive regularization mitigates the distribution gap between the synthetic and real data, and consequently maintains the desired snow-invariant background details. Furthermore, based on the atmospheric scattering model, we introduce a Prior-guided Temporal Decoupling Experts module to decompose the physical components that make up a snowy video in a frame-correlated manner. We evaluate our SemiVDN on benchmark datasets and the collected real snowy data. The experimental results demonstrate the superiority of our approach against state-of-the-art image- and video-level desnowing methods.
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