The second spatiotemporal variant isa “(2+1)D” convolutional block, which explicitly factorizes3D convolution into two separate and successive operations,a 2D spatial convolution and a 1D temporal convolution.

Regardless of the size of output pro-duced by the last convolutional layer, each network appliesglobal spatiotemporal average pooling to the final convolu-tional tensor, followed by a fully-connected (fc) layer per-forming the final classification (the output dimension of thefc layer matches the number of classes, e.g.,400for Kinet-ics).

The first formulation is named mixed con-volution (MC) and consists in employing 3D convolutionsonly in the early layers of the network, with 2D convolu-tions in the top layers.