Temporal Gaussian Mixture Layer for Videos
Abstract
We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in continuous activity videos. The TGM layer is a temporal convolutional layer governed by a much smaller set of parameters (e.g., location/variance of Gaussians) that are fully differentiable. We present our fully convolutional video models with multiple TGM layers for activity detection. The extensive experiments on multiple datasets, including Charades and MultiTHUMOS, confirm the effectiveness of TGM layers, significantly outperforming the state-of-the-arts.
Cite
Text
Piergiovanni and Ryoo. "Temporal Gaussian Mixture Layer for Videos." International Conference on Machine Learning, 2019.Markdown
[Piergiovanni and Ryoo. "Temporal Gaussian Mixture Layer for Videos." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/piergiovanni2019icml-temporal/)BibTeX
@inproceedings{piergiovanni2019icml-temporal,
title = {{Temporal Gaussian Mixture Layer for Videos}},
author = {Piergiovanni, Aj and Ryoo, Michael},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {5152-5161},
volume = {97},
url = {https://mlanthology.org/icml/2019/piergiovanni2019icml-temporal/}
}