object segmentation) literature. This is true in terms of both, datasets (see  [@Geiger2012CVPR; @MOTChallenge2015; @MilanL0RS16] for MOT and [@Perazzi_2016_CVPR; @Prest_2012_CVPR; @Li_2013_ICCV] for VOS/VOT, respectively), and methods tackling the problems ([@sharma2018beyond] for MOT and [@yang2019video; @Luiten_2019_ICCV; @Osep18ICRA; @wang2019fast] for VOS).

#### Combining motion and semantics.

The work in [@NIPS2017_7267] uses semantic segmentation results from [@Long2015] for video frames together with optical flow predictions from EpicFlow [@RevaudWHS15] to serve as *ground truth*, for training a joint model on future scene parsing, the task of anticipating future motion of scene semantics. In [@Luc_2018] this task is extended to deal with future instance segmentation prediction, based on convolutional features from a Mask R-CNN [@He2017] instance segmentation branch. In [@HurR16] a method for jointly estimating optical flow and temporally consistent semantic segmentation from monocular video is introduced. Their approach is based on a piece-wise flow model, enriched with semantic information through label consistency of superpixels.

#### Semi-automated dataset generation.

Current MOT [@wen2015ua; @MilanL0RS16; @Geiger2012CVPR] and VOS [@xu2018youtube; @Pont-Tuset_arXiv_2017] benchmarks are annotated based on human efforts. Some of them, however, use some kind of automation. In [@xu2018youtube], they exploit the temporal correlation between consecutive frames in a skip-frame strategy, although the segmentation masks are manually annotated. 