论文标题
从未标记的视频中学习视频对象细分
Learning Video Object Segmentation from Unlabeled Videos
论文作者
论文摘要
我们为视频对象细分（VOS）提出了一种新的方法，该方法可以从未标记的视频中解决对象模式学习，这与大多数现有的方法不同，这些方法严重依赖于广泛的注释数据。我们引入了一个名为Mug的统一的无监督/弱监督的学习框架，该框架全面捕获了VOS在多个粒度上的内在特性。我们的方法可以帮助提高对VO中的视觉模式的理解，并大大减轻注释负担。凭借精心设计的体系结构和强大的表示能力，我们的学习模型可以应用于不同的VOS设置，包括对象级零击VOS，实例级级零击VOS和One-Shot VOS。实验证明了在这些环境中的表现，以及杯子在利用未标记数据以进一步提高分割精度的潜力。
We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data. We introduce a unified unsupervised/weakly supervised learning framework, called MuG, that comprehensively captures intrinsic properties of VOS at multiple granularities. Our approach can help advance understanding of visual patterns in VOS and significantly reduce annotation burden. With a carefully-designed architecture and strong representation learning ability, our learned model can be applied to diverse VOS settings, including object-level zero-shot VOS, instance-level zero-shot VOS, and one-shot VOS. Experiments demonstrate promising performance in these settings, as well as the potential of MuG in leveraging unlabeled data to further improve the segmentation accuracy.
