@conference {cGonzalez-i-Calabuig, title = {Curriculum Learning for Recurrent Video Object Segmentation}, booktitle = {ECCV 2020 Women in Computer Vision Workshop}, year = {2020}, month = {08/2020}, abstract = {

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.



Curriculum Learning for Recurrent Video Object Segmentation from Universitat Polit{\`e}cnica de Catalunya
}, author = {Gonzalez-i-Calabuig, Maria and Ventura, C. and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xGonzalez-i-Calabuig20, title = {Curriculum Learning for Recurrent Video Object Segmentation}, year = {2020}, abstract = {

Video object segmentation (VOS) is a computer vision task that aims at determining\ the pixels of an object of interest along a video sequence. This thesis explores different\ curriculum learning strategies for a deep neural network trained to solve this task.

Curriculum learning defines a methodology where the training data are not randomly presented to the model, instead, they are organized in a meaningful way. Simple concepts are first presented and gradually become more complex. Four different\ curriculum strategies are explored: schedule sampling, frame skipping, the effect of\ temporal and spatial recurrence variations and loss penalization by the object{\textquoteright}s area.

This work focuses on the RVOS neural architecture, a recurrent architecture originally tested on the DAVIS and YouTube-VOS datasets for one-shot video object segmentation, over the cars class of the KITTI-MOTS dataset. Even though this architecture is a fast solution for the VOS task, the model struggles with the KITTI-MOTS\ dataset, whose videos are more crowded and challenging.

For the schedule sampling curriculum, both the classic and inverse implementations are evaluated. Results show how inverse schedule sampling strategies improve\ the model{\textquoteright}s performance instead of the classic approach, the forward one. The different frame skipping schemes are also beneficial, but only when training with the\ ground truth mask instead of the predicted ones. Lastly, both the curriculums that\ vary the temporal and spatial recurrence or penalize the loss by the object{\textquoteright}s area have\ shown poor model{\textquoteright}s performance.

These results show how curriculum learning strategies affect greatly the performance of recurrent neural networks. Moreover, the results on the inverse schedule\ sampling and frame skipping strategies invite to further explore this schemes to exploit\ their benefits.




Curriculum Learning for Recurrent Video Object Segmentationfrom\ Universitat Polit{\`e}cnica de Catalunya
}, author = {Gonzalez-i-Calabuig, Maria}, editor = {Xavier Gir{\'o}-i-Nieto and Ventura, C.} }