Yes even within a particular video there are lots of frames where the act is implied not directly shown, like a close-up of others faces. Karpathy et al. showed they could still learn from the sports video database even with random crowd shots or announcer shots not being removed.
I think the quality for the data influences the result and hand crafting the dataset is what lead to 95% accuracy on new instances.
I think thats doable. I'll be adding an autotag mode. I've been thinking about other attributes I can detect from race, to hair color, to number of participants.
Crowd sourcing is good except it can't tag new videos no one's seen yet.
This program can also be viewed as a general framework for classifying video with a Caffe model, using batching and threading in C++. By replacing the weights, model definition, and mean file it can immediately be used to edit videos with other classes without recompiling
https://github.com/ryanjay0/miles-deep/raw/master/images/pre...