A semi-automatic approach is proposed in [@berg2019semi] for annotating multi-modal benchmarks. It uses a pre-trained VOS model [@johnander2019generative] but still requires additional manual annotations and supervision on the target dataset. The MOTS training data from [@Voigtlaender_2019_CVPR] is generated by augmenting an existing MOT dataset [@MilanL0RS16; @Geiger2012CVPR] with segmentation masks, using the iterative, human-in-the-loop procedure we briefly described in Sec. \[sec:intro\]. The tracking labels on [@MilanL0RS16; @Geiger2012CVPR] were manually annotated and, in the case of [@MilanL0RS16], refined by adding high-confidence detections from a pedestrian detector.

The procedures used in these semi-automatic annotation approaches often resemble those of VOS systems which require user input to select objects of interest in a video. Some proposed techniques in this field [@chen2018blazingly; @caelles20182018; @maninis2018deep] are extensible to multiple object scenarios. A pixel-level embedding is built in [@chen2018blazingly] to solve segmentation and achieve state-of-the-art accuracy with low annotation and computational cost. To the best of our knowledge, the only work that generates tracking data from unlabeled large-scale videos is found in [@OsepVoigtlaender18ECCVW]. By using an adaptation of the approach in [@Osep18ICRA], Osep perform object discovery on unlabeled videos from the KITTI Raw [@Gei+13] and Oxford RobotCar [@maddern20171] datasets. Their approach however cannot provide joint annotations for 