Abstract
We propose FlowCut, a simple and capable method for unsupervised video instance segmentation consisting of a three-stage framework to construct a high-quality video dataset with pseudo labels. To our knowledge, our work is the first attempt to curate a video dataset with pseudo-labels for unsupervised video instance segmentation. In the first stage, we generate pseudo-instance masks by exploiting the affinities of features from both images and optical flows. In the second stage, we construct short video segments containing high-quality, consistent pseudo-instance masks by temporally matching them across the frames. In the third stage, we use the YouTubeVIS-2021 video dataset to extract our training instance segmentation set, and then train a video segmentation model. FlowCut achieves state-of-the-art performance on the YouTubeVIS-2019, YouTubeVIS-2021, DAVIS-2017, and DAVIS-2017 Motion benchmarks.
Abstract (translated)
我们提出了FlowCut，这是一种简单而强大的无监督视频实例分割方法，通过一个三阶段框架构建带有伪标签的高质量视频数据集。据我们所知，我们的工作是首次尝试为无监督视频实例分割整理带伪标签的数据集。 在第一阶段，我们利用来自图像和光流的特征之间的亲和性生成伪实例掩码。 第二阶段中，我们通过跨帧匹配构造包含高质、一致伪实例掩码的短视频片段。 第三阶段中，我们将使用YouTubeVIS-2021视频数据集提取训练实例分割集合，并训练一个视频分割模型。 FlowCut在YouTubeVIS-2019、YouTubeVIS-2021、DAVIS-2017和DAVIS-2017 Motion基准测试上达到了当前最佳的性能。
URL
https://arxiv.org/abs/2505.13174
