our GTAN (lower). The typical method fixes temporal scale in each feature map and seldom explores temporal structure of an action. In contrast, temporal structure is taken into account in our GTAN through learning a set of Gaussian kernels.[]{data-label="fig1:1"}](intro_fig_camera){width="42.00000%"}

One natural way of temporal action localization is to extend image object detection frameworks, e.g., SSD [@Liu:ECCV16] or Faster R-CNN [@Ren:NIPS15], for producing spatial bounding boxes in a 2D image to temporal localization of an action in a 1D sequence [@Chao:CVPR18; @Lin:MM17]. The upper part of Figure \[fig1:1\] conceptualizes a typical process of one-stage action localization. In general, the frame-level or clip-level features in the video sequence are first aggregated into one feature map, and then multiple 1D temporal convolutional layers are devised to increase the size of temporal receptive fields and predict action proposals. However, the temporal scale corresponding to the cell in each feature map is fixed, making such method unable to capture the inherent temporal structure of an action. As such, one ground-truth action proposal in the green box is detected as three ones in this case. Instead, we propose to alleviate the problem by exploring the temporal structure of an action through learning a Gaussian kernel for 