[BooglyWoo]

"Classical" CV and deep-learning CV needn't be opposing one another.

There are several cases in which the classical approach is emulated by deep networks - implementing the same carefully thought-out pipelines but in a way that leverages representations learned from huge datasets (which are undeniably very powerful).

Some examples are:

* Bags of convolutional features for scalable instance search https://arxiv.org/pdf/1604.04653.pdf

This paper treats each 'pixel' of a CNN activation tensor as a local descriptor, clusters them, and describes an image as a bag-of-visual-words histogram.

* Learned Invariant Feature Transform https://arxiv.org/abs/1603.09114v2

This paper very explicitly emulates the entire SIFT pipeline for computing correspondences across pairs of images

* Inverse compositional spatial transformer networks https://arxiv.org/abs/1612.03897v1

This paper emulates Lucas-Kanade approach to computing the transform between 2 images with differentiable (trainable) components.

Also, don't forget that deformable part models are convolutional networks! https://arxiv.org/abs/1409.5403

[3390778]

Thank you for these great links. I'll add another interesting paper that carries on this emulation program:

Conditional Random Fields as Recurrent Neural Networks https://arxiv.org/abs/1409.5403

I hope more fruit comes out of the fusion deep learning and graphical models.