  Video object segmentation can be understood as a sequence-to-sequence task
that can benefit from the curriculum learning strategies for better and faster
training of deep neural networks. This work explores different schedule
sampling and frame skipping variations to significantly improve the performance
of a recurrent architecture. Our results on the car class of the KITTI-MOTS
challenge indicate that, surprisingly, an inverse schedule sampling is a better
option than a classic forward one. Also, that a progressive skipping of frames
during training is beneficial, but only when training with the ground truth
masks instead of the predicted ones. Source code and trained models are
available at http://imatge-upc.github.io/rvos-mots/.
