each cell, which dynamically indicates a particular interval of an action proposal. A mixture of Gaussian kernels could even be grouped to describe an action, which is more flexible to localize action proposals with various length as illustrated in the bottom part of Figure \[fig1:1\]. More importantly, the contextual information is naturally involved with the feature pooling based on the weights in Gaussian curve.

By delving into temporal structure of an action, we present a novel Gaussian Temporal Awareness Networks (GTAN) architecture for one-stage action localization. Given a video, a 3D ConvNet is utilized as the backbone to extract clip-level features, which are sequentially concatenated into a feature map. A couple of convolutional layers plus max-pooling layer are firstly employed to shorten the feature map and increase the temporal size of receptive fields. Then, a cascaded of 1D temporal convolutional layers (anchor layers) continuously shorten the feature map and output anchor feature map, which consists of features of each cell (anchor). On the top of each anchor layer, a Gaussian kernel is learnt for each cell to dynamically predict a particular interval of an action proposal corresponding to that cell. Multiple Gaussian kernels could even be mixed to capture action 