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For further progress in video object segmentation (VOS), larger, morediverse, and more challenging datasets will be necessary. However, denselylabeling every frame with pixel masks does not scale to large datasets. We usea deep convolutional network to automatically create pseudo-labels on a pixellevel from much cheaper bounding box annotations and investigate how far suchpseudo-labels can carry us for training state-of-the-art VOS approaches. A veryencouraging result of our study is that adding a manually annotated mask inonly a single video frame for each object is sufficient to generatepseudo-labels which can be used to train a VOS method to reach almost the sameperformance level as when training with fully segmented videos. We use thisworkflow to create pixel pseudo-labels for the training set of the challengingtracking dataset TAO, and we manually annotate a subset of the validation set.Together, we obtain the new TAO-VOS benchmark, which we make publicly availableat www.vision.rwth-aachen.de/page/taovos. While the performance ofstate-of-the-art methods on existing datasets starts to saturate, TAO-VOSremains very challenging for current algorithms and reveals their shortcomings.

Reducing the Annotation Effort for Video Object Segmentation Datasets