chaoyuaw
commented
5 years ago Hi Barak,
Sorry for the confusion.
I think part of the confusion comes from the evaluation protocol we follow. The commonly used protocol samples a fixed number of (e.g. 25) "locations" in a test video. However, in practice, the actual number of frames used in different methods might not be strictly the same. For example, TSN samples 25 RGB frames + 25 optical flow stacks, so the actual frames used are actually > 250 frames. For 3D CNNs, eg. non-local nets, the final prediction are calculated by averaging of "clips" , each of which already has 32 frames. But overall, all these methods try to sample a fixed number of "locations" in a test video. Our method does the same. That is, we sample 25 locations for each modality, and combine, similar to what 2-stream networks do. Does it make sense?
Esaada
Hi, in the paper you guys said, that you're sampling 25 frames uniformly. for what I understood , let's say the number of frames is 100, you sample 25 indexes. and those are the frames. in this method I understand why you get 4.2 GFLOPs. But in the code it doesn't look like that, in: https://github.com/chaoyuaw/pytorch-coviar/blob/master/dataset.py#L130 you are running on the number of segemnts, and you calling coviar for each of the reprsentation, I mean you're calling this module for I frames residual and for mv, so you choose in a head how much to sample from each type? I'm a bit confused on this testing method and the code, thanks for your elp.