Not at all! I'd love for vision to be solved, no matter what the method. I'm more than happy to move onto another field if that's the case.
But I don't think it is. MNIST data is not particularly challenging. It's great that deep learning methods work there -- they must be doing something right.
getting best results on the harder
vision challenges is simply a matter
of let the computers run long enough.
Collobert's work for example took
3 months of training. I don't see why
vision challenges should any different.
Perhaps the vision researchers, of which
there are many more people than the few
deep learning groups should try it.
But I don't think it is. MNIST data is not particularly challenging. It's great that deep learning methods work there -- they must be doing something right.
Come back and taunt me when deep learning methods start getting state-of-the-art results on, e.g., Pascal VOC: http://pascallin.ecs.soton.ac.uk/challenges/VOC/