proposals with arbitrary length. Through Gaussian pooling, the features of each cell is upgraded by aggregating the features of contextual cells weighted by the values in the Gaussian curve for final action proposal prediction. The whole architecture is end-to-end optimized by minimizing one classification loss plus two regression losses, i.e., localization loss and overlap loss.

The main contribution of this work is the design of an one-stage architecture GTAN for addressing the issue of temporal action localization in videos. The solution also leads to the elegant view of how temporal structure of an action should be leveraged for detecting actions with various length and how contextual information should be utilized for boosting temporal localization, which are problems not yet fully understood in the literature.

![image](framework_camera){width="88.00000%"}

Related Work
============

We briefly group the related works into two categories: temporal action proposal and temporal action detection. The former focuses on investigating how to precisely localize video segments which contain actions, while the latter further classifies these actions into known classes.

We summarize the approaches on temporal action proposal mainly into two directions: content-independent proposal and content-dependent proposal. The main stream of content-independent proposal algorithms is uniformly or sliding window-ly sampling in a 