Can you elaborate on what in CV became obsolete overnight? I took a survey course in CV but I haven't kept up. You still do facial detection, object recognization, camera calibration, image stitching the same way in 2012? Or has it changed because the processing has gotten faster and the results are near real-time?
These were at the root of many detectors. They still are for some applications but for most of them, a few layers of CNN manage to train far better and very counter-intuitive detectors.
Facial detection/recognition was based on features, this is not my specialty, I don't know if DL got better there too as their features were pretty advanced but if they are not there yet I am sure it is just a matter of time.
I can see image stitching benefiting from a deep-learned filter pass too.
Camera calibration is pretty much a solved problem by now, I don't think DL adds a lot to it.
Like I said, not everything became obsolete, but around 50% of the field was taken over my DL algorithms where, before that, hand-crafted algorithms had usually vastly superior performances.
Just to confirm for the facial recognition/ detection, modern DNN algorithms outperform the 'classic' methods that took decades of continuous improvement ...
