End-To-End Learning of Action Detection From Frame Glimpses in Videos
Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei
Abstract
In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and whether to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's task-specific decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.
BibTeX
@inproceedings{cvpr2016_endtoendlearning,
title = {End-To-End Learning of Action Detection From Frame Glimpses in Videos},
author = {Serena Yeung and Olga Russakovsky and Greg Mori and Li Fei-Fei},
booktitle = {CVPR 2016},
year = {2016}
}