Learning Actions from the Web
Abstract
This paper proposes a generic method for action recognition in uncontrolled videos. The idea is to use images collected from the Web to learn representations of actions and use this knowledge to automatically annotate actions in videos. Our approach is unsupervised in the sense that it requires no human intervention other than the text querying. Its benefits are two-fold: 1) we can improve retrieval of action images, and 2) we can collect a large generic database of action poses, which can then be used in tagging videos. We present experimental evidence that using action images collected from the Web, annotating actions is possible.
Cite
Text
Ikizler-Cinbis et al. "Learning Actions from the Web." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459368Markdown
[Ikizler-Cinbis et al. "Learning Actions from the Web." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/ikizlercinbis2009iccv-learning/) doi:10.1109/ICCV.2009.5459368BibTeX
@inproceedings{ikizlercinbis2009iccv-learning,
title = {{Learning Actions from the Web}},
author = {Ikizler-Cinbis, Nazli and Cinbis, Ramazan Gokberk and Sclaroff, Stan},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2009},
pages = {995-1002},
doi = {10.1109/ICCV.2009.5459368},
url = {https://mlanthology.org/iccv/2009/ikizlercinbis2009iccv-learning/}
}