Have We Forgotten about Geometry in Computer Vision?
I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-learning-is-overrated sort of way. Whereas it seems to me he is merely saying Deep Learning should be informed by Classic CV.<p>&quot;I think we’re running out of low-hanging fruit, or problems we can solve with a simple high-level deep learning API. Specifically, I think many of the next advances in computer vision with deep learning will come from insights to geometry.&quot;<p>And it&#x27;s true. A lot of the low-hanging fruit have been gotten, and stuff like SLAM is not about to be done wholly by deep learning (probably). There are a lot of problems that require more insight and analysis than &#x27;throw a deep network at a large dataset&#x27;. As he concludes<p>&quot;learning complicated representations with deep learning is easier and more effective if the architecture can be structured to leverage the geometric properties of the problem.&quot;<p>And thinking about what the architecture should is basically the hard bit in deep learning.