Thanks for the response. Yeah, I meant that. Why should it be a 2D convolution? Isn’t it possible to deploy a separate 1D convolution for the channels? To make it more clear, consider that we have a tensor (video) with the size of T * C * H * W, where T is the number of time-steps, C the number of channels and H,W are the spatial height and width. I have captured the spatial info using ResNet and temporal info using a 1D temporal convolution. However, there are still some useful information in the semantic subspace which can be extracted by getting the correlations between the channels. Am I able to use a separate 1D channel-wise convolution for that with the kernel size of 3? Why don’t people care about the correlation between the channels?(most of the popular networks like ResNet don’t have that)