0
$\begingroup$

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 =
    t1,[v1_0....v1_n]
    t2,[v2_0....v2_n]
        :
        :
    tm,[vm_0....vm_n]

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".

$\endgroup$
1
$\begingroup$

I would call that a multi-headed convolutional model.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.