testing set.

Experimental Settings
---------------------

**Implementations.** We utilize Pseudo-3D [@Qiu:ICCV17] network as our 3D backbone. The network input is a 16-frame clip and the sample rate of frames is set as $8$. The 2,048-way outputs from pool5 layer are extracted as clip-level features. Table \[table3:1\] summarizes the structures of 1D anchor layers. Moreover, we choose three temporal scale ratios $\{r_d\}_{d=1}^{3}=[2^0, 2^{1/3}, 2^{2/3}]$ derived from [@LinYi:ICCV17]. The IoU threshold $\varepsilon$ in Gaussian grouping is set as $0.7$ by cross validation. The balancing parameters $\beta$ and $\gamma$ are also determined on a validation set and set as $2.0$ and $75$. $\xi$ and $\rho$ are set as $0.8$ and $0.75$ in soft-NMS. The parameter $\alpha_1$ and $\alpha_2$ are all set as $1.0$ by cross validation. We implement GTAN on Caffe [@Yang:caffe] platform. In all the experiments, our networks are trained by utilizing stochastic gradient descent (SGD) with $0.9$ momentum. The initial learning rate is set as $0.001$, and decreased by $10\%$ after every $2.5k$ iterations on THUMOS14 and $10k$ iterations on ActivityNet. The mini-batch size is $16$ and the weight decay parameter is $0.0001$.

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