instance with the maximum ranking score $y_{f_m}$ as $\phi_m$. The ranking score $y_{f_k}$ of other instance $\phi_k$ will be decreased or not, according to the IoU computed with $\phi_m$: $$\label{Eq2:11}
\small
y'_{f_k} =
\begin{cases}
~~~y_{f_k}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~,~~ \text{\footnotesize if~~$iou(\phi_k,\phi_m)< \rho$} \\
~~~y_{f_k} \cdot e^{-\frac{iou(\phi_k,\phi_m)^2}{\xi}}~~,~~ \text{\footnotesize if~~$iou(\phi_k,\phi_m) \geq \rho$}
\end{cases},$$ where $\xi$ is the decay parameter and $\rho$ is the NMS threshold.

Experiments {#sec:EX}
===========

We empirically verify the merit of our GTAN by conducting the experiments of temporal action localization on two popular video recognition benchmarks, i.e., ActivityNet v1.3 [@ActivityNet] and THUMOS14 [@Thumos].

Datasets
--------

The **ActivityNet v1.3** dataset contains 19,994 videos in 200 classes collected from YouTube. The dataset is divided into three disjoint subsets: training, validation and testing, by 2:1:1. All the videos in the dataset have temporal annotations. The labels of testing set are not publicly available and the performances of action localization on ActivityNet dataset are reported on validation set. The **THUMOS14** dataset has 1,010 videos for validation and 1,574 videos for testing from 20 classes. Among all the videos, there are 220 and 212 videos with temporal annotations in validation and testing set, respectively. Following [@Xiong:ICCV17], we train the model on validation set and perform evaluation on 