anchor in every anchor layer and dynamically predicts temporal scale of the action proposal corresponding to each anchor. The grouping of multiple Gaussian kernels makes the temporal scale more flexible. Even with a small number of anchor layers, our GTAN should be more responsible to localize action proposals with various length in theory. Figure \[fig4:2\] empirically compares the performances between SSAD and our GTAN on ActivityNet v1.3 when capitalizing on different number of anchor layers. As indicated by the results, GTAN consistently outperforms SSAD across different depths of anchor layers from 4 to 8 on both temporal action proposal and localization tasks. In general, more anchor layers provide better AUC and mAP performances. It is expected that the performance of SSAD decreases more sharply than that of GTAN when reducing the number of anchor layers. In the extreme case of 4 layers, GTAN still achieves 26.77% in average mAP while SSAD only reaches 5.12%, which again confirms the advantage of exploring temporal structure and predicting temporal scale of action proposals.

Comparisons with State-of-the-Art
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[[c|c@[        ]{}c@[        ]{}c@[        ]{}c@[        ]{}c]{}]{}\
& 0.1 & 0.2 & 0.3 & 0.4 & 0.5\
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& 18.2 & 17.0 & 14.0 & 11.7 & 