and ActivityNet v1.3 datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, GTAN achieves 1.9% and 1.1% improvements in mAP on testing set of the two datasets.'
author:
- |
    Fuchen Long$^{\dag}$, Ting Yao$^{\ddag}$, Zhaofan Qiu$^{\dag}$, Xinmei Tian$^{\dag}$, Jiebo Luo$^{\S}$ and Tao Mei$^{\ddag}$\
    $^{\dag}$University of Science and Technology of China, Hefei, China\
    $^{\ddag}$JD AI Research, Beijing, China\
    $^{\S}$University of Rochester, Rochester, NY USA\
    [{longfc.ustc, tingyao.ustc, zhaofanqiu}@gmail.com; xinmei@ustc.edu.cn;]{}\
    [ jluo@cs.rochester.edu; tmei@live.com]{}\
bibliography:
- 'egbib.bib'
title: 'Gaussian Temporal Awareness Networks for Action Localization[^1]'
---

Introduction
============

With the tremendous increase of online and personal media archives, people are generating, storing and consuming a large collection of videos. The trend encourages the development of effective and efficient algorithms to intelligently parse video data. One fundamental challenge that underlies the success of these advances is action detection in videos from both temporal aspect [@Gaidon:PAMI13; @Geest:ECCV16; @Lea:CVPR17; @Shou:CVPR16; @yao2017msr; @Xiong:ICCV17] and spatio-temporal aspect [@Gkioxari:CVPR15; @Dong:ECCV18]. In this work, the main focus is temporal action detection/localization, which is to locate the exact time stamps of the starting and the ending of an action, and recognize the action with a set of categories.

![The intuition of a typical one-stage action localization (upper) and 