I would hardly say that deep learning has taken over - some of the best results in the last few years have come from 'classical' domains like nonlinear optimization.

For example, LSD-SLAM: http://vision.in.tum.de/research/vslam/lsdslam

Deep learning / ML approaches certainly have a place, and they're getting a lot of attention right now, but the computer vision domain is about a lot more than segmentation and classification.

Maybe in coming years we'll see some more breakthroughs from the ML side on encoding priors - for example, teaching a network about projective geometry is a lot worse than just structuring it in a way that it 'knows' what projective geometry is. This could result in a closer collaboration between the two fields.