Really? My impression is that most new grad students have a superficial understanding of this stuff, which they promptly never use, since they just end-to-end deep learning solutions.

I think it depends on the student and researcher. I would agree that most are implementing ANNs to "brute force" around existing fundamental problems - but to me that's as much a solution as doing it another way - I'm not sure it's worth the effort to do it otherwise. Maybe some efficiencies, but I think getting towards are more generalizable ANN based machine vision system outweighs the negatives.

Nah it's just the latest hype to deep learn everything with lots of variations. It'll hit it a local maxima soon of what's achievable and then will begin the task of integrating actual computer vision knowledge.

Isn't DNN simply the way how we currently implement pattern recognition by statistical learning? I mean, you can do it a bunch of other ways too, but at the moment it seems that e.g. pattern recognition by a convolutional net trained on lots of unlabeled images is a better way that many classic algorithmic approaches to detecting specific patterns/features.