Have We Forgotten about Geometry in Computer Vision?
My apprehension was that the computer vision community has been suffering some serious cognitive dissonance lately because here they spent all these years mapping problems to feature spaces of manageable dimensionality, backed by theory saying that proper assumptions must be made to reduce the search space; and then comes these deep nets, hardly tailored to the problems, and out-performs algorithms with decade old history of fine-tuning.Despite this, I don't think anyone disputes the potential of a good set of assumptions. Instead I think what deep learning has thought us is that we should reconsider what these assumptions should be. While geometry might well be the first kind of language a toddler learns to think in, this should probably not be confused with the rigorous geometry of Euclid. Quite possibly we have some spatial relationships such as the affine transformations hard-coded in our brain at birth, but this does not mean, for instance, that one is therefore necessarily ever able to to draw a house in correct perspective.