Chris Ying, Katerina Fragkiadaki. Depth-Adaptive Computational Policies for Efficient Visual Tracking. In Marcello Pelillo, Edwin R. Hancock, editors, Energy Minimization Methods in Computer Vision and Pattern Recognition - 11th International Conference, EMMCVPR 2017, Venice, Italy, October 30 - November 1, 2017, Revised Selected Papers. Volume 10746 of Lecture Notes in Computer Science, pages 109-122, Springer, 2017. [doi]
@inproceedings{YingF17,
title = {Depth-Adaptive Computational Policies for Efficient Visual Tracking},
author = {Chris Ying and Katerina Fragkiadaki},
year = {2017},
doi = {10.1007/978-3-319-78199-0_8},
url = {https://doi.org/10.1007/978-3-319-78199-0_8},
researchr = {https://researchr.org/publication/YingF17},
cites = {0},
citedby = {0},
pages = {109-122},
booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition - 11th International Conference, EMMCVPR 2017, Venice, Italy, October 30 - November 1, 2017, Revised Selected Papers},
editor = {Marcello Pelillo and Edwin R. Hancock},
volume = {10746},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
isbn = {978-3-319-78199-0},
}