  Traditional 3D convolutions are computationally expensive, memory intensive,
and due to large number of parameters, they often tend to overfit. On the other
hand, 2D CNNs are less computationally expensive and less memory intensive than
3D CNNs and have shown remarkable results in applications like image
classification and object recognition. However, in previous works, it has been
observed that they are inferior to 3D CNNs when applied on a spatio-temporal
input. In this work, we propose a convolutional block which extracts the
spatial information by performing a 2D convolution and extracts the temporal
information by exploiting temporal differences, i.e., the change in the spatial
information at different time instances, using simple operations of shift,
subtract and add without utilizing any trainable parameters. The proposed
convolutional block has same number of parameters as of a 2D convolution kernel
of size nxn, i.e. n^2, and has n times lesser parameters than an nxnxn 3D
convolution kernel. We show that the 3D CNNs perform better when the 3D
convolution kernels are replaced by the proposed convolutional blocks. We
evaluate the proposed convolutional block on UCF101 and ModelNet datasets.
