but also how the contextual information can be better leveraged for action localization.

Gaussian Temporal Awareness Networks
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In this section we present the proposed Gaussian Temporal Awareness Networks (GTAN) in detail. Figure \[fig2:1\] illustrates an overview of our architecture for action localization. It consists of two main components: a base feature network and a cascaded of 1D temporal convolutional layers with Gaussian kernels. The base feature network is to extract feature map from sequential video clips, which will be fed into cascaded 1D convolutional layers to generate multiple feature maps in different temporal resolution. For each cell in one feature map, a Gaussian kernel is learnt to control temporal scale of an action proposal corresponding to that cell as training proceeds. Furthermore, a Gaussian Kernel Grouping algorithm is devised to merge multiple Gaussian kernels with high overlap to a larger one for capturing long actions with arbitrary length. Specifically, each action proposal is generated by aggregating the features of contextual cells weighted by the values in the Gaussian curve. The whole network is jointly optimized with action classification loss plus two regression losses, i.e., localization loss and overlap loss, which are utilized to learn action category label, default temporal 