as follows. First, two segmentation masks per object were generated by human annotators (which also had to chose the objects based on diversity in the first place). Then, DeepLabV3+ [@Chen2018ECCV], a state-of-the-art semantic segmentation network, was trained on the initially generated masks in a way to overfit these objects in a sequence-specific way, thus yielding reasonable segmentation masks for the remaining objects in each track. The resulting segmentation masks then underwent another manual correction step to fix remaining errors. Finally, these automatic and manual steps were iterated until convergence.

Including an automated segmentation algorithm is clearly speeding up the MOTS dataset generation process, but still has significant shortcomings. Most importantly, the approach in [@Voigtlaender_2019_CVPR] still depends on the availability of bounding-box based, multi-object tracking information as provided by datasets like [@Geiger2012CVPR; @MilanL0RS16]. Also, their remaining human annotation effort for generating initial instance masks was considerable,  [@Voigtlaender_2019_CVPR] reports that $\approx$8k masks (or $12.3\%$ of all masks in [KITTI MOTS]{}) had been manually labeled before fine-tuning a (modified) DeepLabV3+ segmentation network, pre-trained on COCO [@LinMSCOCO2014] and Mapillary Vistas [@MVD2017]. Finally, such an approach cannot be expected to generalize well for generating orders of magnitude more training data.

In this paper we introduce 