Vimeo-90k Dataset 一个大规模、高质量的视频数据集|视频处理数据集|计算机视觉数据集

一个大规模、高质量的视频数据集Vimeo90K。这个数据集由从vimeo.com下载的8.98万个视频片段组成，其中包含大量场景和动作。它设计用于以下四个视频处理任务：时间帧插值、视频去噪、视频去块和视频超分辨率。 Vimeo90K数据集结构： 1、Triplet dataset (for temporal frame interpolation): The triplet dataset consists of 73,171 3-frame sequences with a fixed resolution of 448 x 256, extracted from 15K selected video clips from Vimeo-90K. This dataset is designed for temporal frame interpolation. Download links are 2、Septuplet dataset (for video denoising, deblocking, and super-resoluttion): Notice: we have recently updated our testing denoising dataset to fix a bug in denoising test data generation. The new quantitative result of our algorithm is reported in our updated paper The septuplet dataset consists of 91,701 7-frame sequences with fixed resolution 448 x 256, extracted from 39K selected video clips from Vimeo-90K. This dataset is designed to video denoising, deblocking, and super-resolution. Abstract Many video processing algorithms rely on optical flow to register different frames within a sequence. However, a precise estimation of optical flow is often neither tractable nor optimal for a particular task. In this paper, we propose task-oriented flow (TOFlow), a flow representation tailored for specific video processing tasks. We design a neural network with a motion estimation component and a video processing component. These two parts can be jointly trained in a self-supervised manner to facilitate learning of the proposed TOFlow. We demonstrate that TOFlow outperforms the traditional optical flow on three different video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution. We also introduce Vimeo-90K, a large-scale, high-quality video dataset for video processing to better evaluate the proposed algorithm. @article{xue2019video, title={Video Enhancement with Task-Oriented Flow}, author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T}, journal={International Journal of Computer Vision (IJCV)}, volume={127}, number={8}, pages={1106--1125}, year={2019}, publisher={Springer} }