  In this paper, we present a comprehensive study and propose several novel
techniques for implementing 3D convolutional blocks using 2D and/or 1D
convolutions with only 4D and/or 3D tensors. Our motivation is that 3D
convolutions with 5D tensors are computationally very expensive and they may
not be supported by some of the edge devices used in real-time applications
such as robots. The existing approaches mitigate this by splitting the 3D
kernels into spatial and temporal domains, but they still use 3D convolutions
with 5D tensors in their implementations. We resolve this issue by introducing
some appropriate 4D/3D tensor reshaping as well as new combination techniques
for spatial and temporal splits. The proposed implementation methods show
significant improvement both in terms of efficiency and accuracy. The
experimental results confirm that the proposed spatio-temporal processing
structure outperforms the original model in terms of speed and accuracy using
only 4D tensors with fewer parameters.
