论文标题
减少视频对象分割数据集的注释工作
Reducing the Annotation Effort for Video Object Segmentation Datasets
论文作者
论文摘要
为了在视频对象细分（VOS）中进一步进展，需要更大，更多样化和更具挑战性的数据集。但是，用像素蒙版将每个帧密集地标记并不能扩展到大型数据集。我们使用深层卷积网络从更便宜的边界框注释中自动创建像素级别的伪标签，并研究此类伪标签可以带我们来训练最先进的VOS方法。我们研究的一个非常令人鼓舞的结果是，仅在每个对象的一个视频框架中添加手动注释的面具就足以生成伪标签，可用于训练VOS方法，以达到与完全分段视频进行训练时达到几乎相同的性能水平。我们使用此工作流来创建Pseudo-Labels，以用于挑战性跟踪数据集TAO的训练集，并手动注释验证集的子集。我们一起获得了新的Tao-Vos基准，我们可以在www.vision.rwth-aachen.de/page/taovos上公开提供。尽管现有数据集上最新方法的性能开始饱和，但在当前算法中，道沃斯仍然非常具有挑战性，并揭示了它们的缺点。
For further progress in video object segmentation (VOS), larger, more diverse, and more challenging datasets will be necessary. However, densely labeling every frame with pixel masks does not scale to large datasets. We use a deep convolutional network to automatically create pseudo-labels on a pixel level from much cheaper bounding box annotations and investigate how far such pseudo-labels can carry us for training state-of-the-art VOS approaches. A very encouraging result of our study is that adding a manually annotated mask in only a single video frame for each object is sufficient to generate pseudo-labels which can be used to train a VOS method to reach almost the same performance level as when training with fully segmented videos. We use this workflow to create pixel pseudo-labels for the training set of the challenging tracking dataset TAO, and we manually annotate a subset of the validation set. Together, we obtain the new TAO-VOS benchmark, which we make publicly available at www.vision.rwth-aachen.de/page/taovos. While the performance of state-of-the-art methods on existing datasets starts to saturate, TAO-VOS remains very challenging for current algorithms and reveals their shortcomings.
