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I would hardly say that deep learning has taken over - some of the best results in the last few years have come from 'classical' domains like nonlinear optimiza
by atroyn 10y ago
I would hardly say that deep learning has taken over - some of the best results in the last few years have come from 'classical' domains like nonlinear optimization.
For example, LSD-SLAM: http://vision.in.tum.de/research/vslam/lsdslam http://vision.in.tum.de/research/vslam/lsdslam
Deep learning / ML approaches certainly have a place, and they're getting a lot of attention right now, but the computer vision domain is about a lot more than segmentation and classification.
Maybe in coming years we'll see some more breakthroughs from the ML side on encoding priors - for example, teaching a network about projective geometry is a lot worse than just structuring it in a way that it 'knows' what projective geometry is. This could result in a closer collaboration between the two fields.
- bluejellybean 10y agoWow, the video you linked is awesome. I would love to see how it would handle a huge dataset of something like time square footage.
- atroyn 10y agoYou might be interested in Prof. Marc Pollefeys' work: http://www.bbc.co.uk/news/technology-11827854 http://www.bbc.co.uk/news/technology-11827854
- joe_the_user 10y agoI would hardly say that deep learning has taken over - some of the best results in the last few years have come from 'classical' domains like nonlinear optimization. Well, the article is arguing deep learning has taken most of the "mindshare", the attention of most researchers. If great results are coming from other parts of the field, that would be a reason to be concerned.