I have some sequential data (e.g. audio, video, text etc.) and I am using this approach to classify sequences. I am sure there's a name for it, but I can't think of it:

vectors =

where t1..tm are the time offsets and the VM are the feature vectors.

Out of this data, I create oversampling by using different window_size, and step_size

[t0,win_size,step_size, np.median(vectors[frame_id:frame_id+win_size],axis=0)]

win_size = the size of the window to mean/median ahead
step_size = how many columns to move with each step 
np.median(vectors[frame_id:frame_id+win_size],axis=0) = column0wise mean or median across the array as a resultant of the vectors

and use the above to train a classifier and should be to generate predictions at multiple scales (e.g. at a large window size).

Edit: for predictions, from the query clip extract a similar set of vectors and then predictions can be aggregated somehow to generate the "consensus".


1 Answer 1


I would call that a multi-headed convolutional model.


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