& 49.4 & 31.6\
& 25.9 & 38.2 & 54.2 & 34.3\

\[table4:3\]

Next, we study how each design in GTAN influences the overall performance on temporal action localization task. Fixed Scale simply employs a fixed temporal interval for each cell or anchor in an anchor layer and such way is adopted in SSAD. Gaussian Kernel leverages the idea of learning one Gaussian kernel for each anchor to model temporal structure of an action and dynamically predict temporal scale of each action proposal. Gaussian Grouping further mixes multiple Gaussian kernels to characterize action proposals with various length. In the latter two cases, Gaussian pooling is utilized to augment the features of each anchor with contextual information.

Table \[table4:2\] details the mAP performances by considering one more factor in GTAN on both datasets. Gaussian Kernel successfully boosts up the mAP performance from 33.5% to 37.1% and from 29.8% to 31.6% on THUMOS14 and ActivityNet v1.3, respectively. This somewhat reveals the weakness of Fixed Scale, where the temporal scale of each anchor is independent of temporal property of the action proposal. Gaussian Kernel, in comparison, models temporal structure and predicts a particular interval for each anchor on the fly. As such, the 