@inproceedings{08036afe701243e597dbd07f75103089,
title = "Predicting future instance segmentation by forecasting convolutional features",
abstract = "Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the “detection head” of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures.",
keywords = "Convolutional neural networks, Deep learning, Instance segmentation, Video prediction",
author = "Pauline Luc and Camille Couprie and Yann LeCun and Jakob Verbeek",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01240-3_36",
language = "English (US)",
isbn = "9783030012397",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "593--608",
editor = "Martial Hebert and Vittorio Ferrari and Cristian Sminchisescu and Yair Weiss",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
}