FlowCut：基于时序掩码匹配的无监督视频实例分割
FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching
AI总结：
本研究提出无监督视频实例分割方法FlowCut，通过三阶段框架构建带伪标签的数据集，在多个基准上实现了当前最优性能。
摘要：
我们提出FlowCut，一种简单有效的无监督视频实例分割方法，它包含一个三阶段框架，用于构建带伪标签的高质量视频数据集。据我们所知，本研究是首次为无监督视频实例分割整理带伪标签的视频数据集的尝试。第一阶段，我们利用图像和光流的特征亲和性生成伪实例掩码。第二阶段，我们通过跨帧时序匹配，构建包含高质量、一致性伪实例掩码的短视频片段。第三阶段，我们使用YouTubeVIS-2021视频数据集提取训练用的实例分割集，随后训练一个视频分割模型。FlowCut在YouTubeVIS-2019、YouTubeVIS-2021、DAVIS-2017以及DAVIS-2017 Motion基准上取得了当前最优性能。
英文摘要：
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.
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。
