  We introduce a new convolutional layer named the Temporal Gaussian Mixture
(TGM) layer and present how it can be used to efficiently capture longer-term
temporal information in continuous activity videos. The TGM layer is a temporal
convolutional layer governed by a much smaller set of parameters (e.g.,
location/variance of Gaussians) that are fully differentiable. We present our
fully convolutional video models with multiple TGM layers for activity
detection. The extensive experiments on multiple datasets, including Charades
and MultiTHUMOS, confirm the effectiveness of TGM layers, significantly
outperforming the state-of-the-arts.
