@inproceedings{60e6b85a35dd403f9b2d08457f3a1505,
title = "Pseudo Ground Truth Segmentation Mask to Improve Video Prediction Quality",
abstract = "Video prediction to foresee future events is an extremely difficult job since it involves spatial feature extraction and temporal sequential analysis. We identified that the semantic information is actually crucial to prediction, and proposed using 'pseudo ground truth' segmentation masks which are generated automatically in real time and add them to the input layers as extra information to predict future frames. Experiments conducted on our self-defined network demonstrated drastically higher quality predictions are achieved when compared with other state-of-the-art direct video prediction models. ",
keywords = "segmentation, video prediction",
author = "Hsu, {Mu Chien} and Shyur, {Jui Chun} and Hiroshi Watanabe",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Global Conference on Consumer Electronics, GCCE 2020 ; Conference date: 13-10-2020 Through 16-10-2020",
year = "2020",
month = oct,
day = "13",
doi = "10.1109/GCCE50665.2020.9292008",
language = "English",
series = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "831--832",
booktitle = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",
}