temporal localization or boundary of each action proposal is more accurate. Moreover, the features of each action proposal are simultaneously enhanced by contextual aggregation through Gaussian pooling and lead to better action classification. Gaussian grouping further contributes a mAP increase of 1.1% and 2.7%, respectively. The results verify the effectiveness and flexibility of mixing multiple Gaussian kernels to capture action proposals with arbitrary length. To better validate the impact of Gaussian grouping, we additionally evaluate GTAN on long action proposals. Here, we consider actions longer than 128 frames in THUMOS14 and 2048 frames in ActivityNet v1.3 as long actions, since the average duration of action instances in THUMOS14 is $\sim$4 seconds which is much smaller than that ($\sim$50 seconds) of ActivityNet. Table \[table4:3\] shows the mAP comparisons between GTAN and its variant GTAN$^-$ which excludes Gaussian grouping. As expected, larger degree of improvement is attained on long action proposals by involving Gaussian grouping.

Evaluation on the Number of Anchor Layers
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In existing one-stage methods, e.g., SSAD, temporal scale is fixed in each anchor layer and the expansion of multiple temporal scales is implemented through increasing the number of anchor layers. Instead, our GTAN learns one Gaussian kernel for each 