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[Music] this video will explain video classification with convolutional neural networks the overview of the presentation is as follows first we can talk about video data what makes collecting video data so challenging to why each individual instance is so large file size then well talk about the spatial temporal CNNs pictured here that are presented in the paper then well talk about multi resolution models and how the author has achieved a four times speed up by using multi resolution streams it looks like about how data augmentation is used in video classification dataset noise and the video data sets and then the overall spatial temporal multi resolution model results in video classification achieving big datasets is very difficult this isnt just due to labeling problems but also mainly due to the storage size so a video compared to an image is a stack of frames so if you have a 200 by 200 resolution with RGB you have 200 by 200 by 3 pixels in each pixel it needs 8 bytes to stor