Open zycironic opened 6 years ago
Thanks for your interest in our work.
K
is the nunmber of proposals after reasoning about a segment of length T
. In DAPs, we slide the model and applied on multiple chunks of length T
. If you need more proposals, you can reduce the striding. Effectively, you are still processing the video stream only once.
If I slide the model for n times, there will be n*K anchor segments matching the prior K anchor segments?
I'm sorry for your confusion.
The anchors spanned by the model belongs to the time interval T
. If you slide it by delta
, the following K
predictions will be in the interval [delta, T+delta]
. In other word, the anchors are parametrized in terms of T.
Please, take a look at the inference code. It's simple to understand there.
Hello @escorciav , I 'm curious about how you prepare your training data. In the network, the structure is pre-defined (the output dimension is always K for giving K proposals). However, during the training, for a audio clip of T, what if the number of activities is less than K? How do you assign the ground truth label? Thanks a lot. And this work is really interesting.
I don't understand how much proposals are generated in all in one video.During training, only one stream is processed in one video,so the overall number of proposals is K.Is that true?