HyperCUT：使用无监督排序从单张模糊图像生成视频序列
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
AI总结：
本文提出HyperCUT，一种自监督排序方案，通过将视频序列映射到高维空间并利用超平面划分正反向序列来解决帧顺序模糊问题，实现高质量的图像到视频去模糊。
摘要：
我们考虑训练图像到视频去模糊模型的挑战性任务，旨在恢复与给定模糊图像输入相对应的清晰图像序列。困扰图像到视频模型训练的一个关键问题是帧顺序的模糊性，因为正向和反向序列都是合理的解。本文提出了一种有效的自监督排序方案，允许训练高质量的图像到视频去模糊模型。与以前依赖顺序不变损失的方法不同，我们为每个视频序列分配明确的顺序，从而避免了顺序模糊问题。具体来说，我们将每个视频序列映射到潜在高维空间中的一个向量，使得存在一个超平面，对于每个视频序列，从它及其反向序列中提取的向量位于超平面的不同侧。向量的侧别将用于定义相应序列的顺序。最后，我们提出了一个用于图像到视频去模糊问题的真实图像数据集，涵盖各种流行的领域，包括人脸、手和街道。大量实验结果证实了我们方法的有效性。代码和数据可在 https://github.com/VinAIResearch/HyperCUT.git 获取
英文摘要：
We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://github.com/VinAIResearch/HyperCUT.git
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
