The SGD we use today actually has some strong ties back to that second order optimization work - see some papers by Ilya Sutskever relating a special form of momentum back to second order methods like HF. http://www.cs.utoronto.ca/~ilya/pubs/2013/1051_2.pdf His dissertation covers this at some length as well.
Using Adagrad, Adadelta, etc. isn't really SGD as it was back in 2012, and this years entrant "GoogLeNet" basically halved the error again using these and other tricks (we think) - which is even more impressive considering 11% - 6.7% is a HUGE increase in difficulty, just my 2 cents.
However, there is a good reason the colloquial name for these things is "Alexnets"... that work was truly incredible and has not stopped behind the doors of Google I don't think.
The scientific revolution in computer vision right now is due to deep convnets, trained in a supervised way using backprop and SGD. All of the systems we're talking about that have started blowing away records are members of this family, and were trained this way. If Alex Krizhevsky had not entered ImageNet 2012, we would not be having this conversation (in part because I probably would never have gotten curious enough about it to leave my home territories of systems and programming languages). Second order methods, unsupervised pre-training, RBMs, graphical models, etc. etc. etc. were exciting for those inside the field, definitely provided encouragement to those optimistic about deep models, and still might prove important, but they have had little impact and visibility to skeptical people outside, in the way that entering a computer vision competition and murdering all the computer vision systems did.