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I'm very skeptical that there is any merit to the 'tracking' over other techniques, as well as the colorization being better than this 14 year old paper: http:

I'm very skeptical that there is any merit to the 'tracking' over other techniques, as well as the colorization being better than this 14 year old paper: http://webee.technion.ac.il/people/anat.levin/papers/colorization-siggraph04.pdf http://webee.technion.ac.il/people/anat.levin/papers/coloriz... The results in their videos look very poor.

IanCal 8y ago
Tracking is not the method they're using for colourizing, but the other way around. Your linked paper has no tracking.
- AboutTheWhisles 8y ago
  I realize that. They are for some reason saying they can track things with their colorization, when their colorization is extremely unimpressive as well as the tracking that results from using it. There is no reason colorization needs to happen to do the tracking anyway. The tracking is unimpressive and now indirect. This isn't some sort of epiphany they've discovered, they are just reinventing video image segmentation very poorly. Here are half a dozen examples from a 30 second google search: https://www.youtube.com/watch?v=juDvLrFQF0U https://www.youtube.com/watch?v=juDvLrFQF0U https://www.youtube.com/watch?v=JYgyDdLf7GQ https://www.youtube.com/watch?v=JYgyDdLf7GQ https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36247.pdf https://static.googleusercontent.com/media/research.google.c... https://perso.liris.cnrs.fr/nicolas.bonneel/InteractiveMultilabelVideoSegmentation.pdf https://perso.liris.cnrs.fr/nicolas.bonneel/InteractiveMulti... http://files.is.tue.mpg.de/black/papers/TsaiCVPR2016.pdf http://files.is.tue.mpg.de/black/papers/TsaiCVPR2016.pdf https://graphics.ethz.ch/~perazzif/bvs/files/bvs.pdf https://graphics.ethz.ch/~perazzif/bvs/files/bvs.pdf The only reason this is news is because it's google and the researchers seem to think they've discovered something. Techniques like this with much better results have been shown at Siggraph for decades.
  - felippee 8y ago
    Right, I totally agree. What is more, I think this same result could actually be sold without the pompous deep learning bullshit and be received quite differently. If they did not claim to invent the wheel, but rather modestly noted their observation (which in a limited way is actually quite cool - that is from a known - at least on this forum - deep learning skeptic like me), it would make a much better impression. Same is true actually for many DL papers. They'd be actually cool, if they weren't oversold.
    - dimatura 8y ago
      What is the "pompous deep learning bullshit"? They don't really dwell a lot on DL itself, just describe a reasonable architecture for the task and how they train it. While it's possible to conceive the same concept realized with other machine learning methods (e.g. Random Forest or linear embeddings), it's hard to imagine it would work as well. As for overselling, I'd say that it's somewhat standard writing style in CS academia to oversell[1] but this is hardly an example of that. [1] It'd be unusual, but admittedly refreshing for a paper to say "this is just an incremental tweak on existing methods" or something along those lines.
      - felippee 8y ago
        No, there is something special to machine learning and AI in general. E.g. SIGGRAPH papera are different. No one there claims to solve more than they actually do solve. DL is soaked with hype and self congratulatory BS. The best way to spot it is to check the citations. Typically they solve an already solved problem, skipping entirely any pre deep learning literature on it (or if they do cite it, only to dump BS on it) and then just cite a few of their own more or less relevant papers. I'm aware I'm overgeneralizing here and not every paper is like that, but I've seen enough to detect a trend. It is as if defending or advertising "deep learning" was the purpose of the paper. It is not. The purpose of a paper is to show a solution to a problem. Much of DL literature (again not all) is a "solution in a desperate search of a problem" rather than the opposite. I think many of these papers (including this one) would make a great blog post, but just isn't quite enough in terms of scientific content for a full blown paper. A curiosity, nice gimmick, but nothing more. Not really a solution to a problem, not really any idea of non trivial universality.

IanCal 8y ago

Tracking is not the method they're using for colourizing, but the other way around. Your linked paper has no tracking.

AboutTheWhisles 8y ago
I realize that. They are for some reason saying they can track things with their colorization, when their colorization is extremely unimpressive as well as the tracking that results from using it. There is no reason colorization needs to happen to do the tracking anyway. The tracking is unimpressive and now indirect. This isn't some sort of epiphany they've discovered, they are just reinventing video image segmentation very poorly. Here are half a dozen examples from a 30 second google search: https://www.youtube.com/watch?v=juDvLrFQF0U https://www.youtube.com/watch?v=juDvLrFQF0U https://www.youtube.com/watch?v=JYgyDdLf7GQ https://www.youtube.com/watch?v=JYgyDdLf7GQ https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36247.pdf https://static.googleusercontent.com/media/research.google.c... https://perso.liris.cnrs.fr/nicolas.bonneel/InteractiveMultilabelVideoSegmentation.pdf https://perso.liris.cnrs.fr/nicolas.bonneel/InteractiveMulti... http://files.is.tue.mpg.de/black/papers/TsaiCVPR2016.pdf http://files.is.tue.mpg.de/black/papers/TsaiCVPR2016.pdf https://graphics.ethz.ch/~perazzif/bvs/files/bvs.pdf https://graphics.ethz.ch/~perazzif/bvs/files/bvs.pdf The only reason this is news is because it's google and the researchers seem to think they've discovered something. Techniques like this with much better results have been shown at Siggraph for decades.
- felippee 8y ago
  Right, I totally agree. What is more, I think this same result could actually be sold without the pompous deep learning bullshit and be received quite differently. If they did not claim to invent the wheel, but rather modestly noted their observation (which in a limited way is actually quite cool - that is from a known - at least on this forum - deep learning skeptic like me), it would make a much better impression. Same is true actually for many DL papers. They'd be actually cool, if they weren't oversold.
  - dimatura 8y ago
    What is the "pompous deep learning bullshit"? They don't really dwell a lot on DL itself, just describe a reasonable architecture for the task and how they train it. While it's possible to conceive the same concept realized with other machine learning methods (e.g. Random Forest or linear embeddings), it's hard to imagine it would work as well. As for overselling, I'd say that it's somewhat standard writing style in CS academia to oversell[1] but this is hardly an example of that. [1] It'd be unusual, but admittedly refreshing for a paper to say "this is just an incremental tweak on existing methods" or something along those lines.
    - felippee 8y ago
      No, there is something special to machine learning and AI in general. E.g. SIGGRAPH papera are different. No one there claims to solve more than they actually do solve. DL is soaked with hype and self congratulatory BS. The best way to spot it is to check the citations. Typically they solve an already solved problem, skipping entirely any pre deep learning literature on it (or if they do cite it, only to dump BS on it) and then just cite a few of their own more or less relevant papers. I'm aware I'm overgeneralizing here and not every paper is like that, but I've seen enough to detect a trend. It is as if defending or advertising "deep learning" was the purpose of the paper. It is not. The purpose of a paper is to show a solution to a problem. Much of DL literature (again not all) is a "solution in a desperate search of a problem" rather than the opposite. I think many of these papers (including this one) would make a great blog post, but just isn't quite enough in terms of scientific content for a full blown paper. A curiosity, nice gimmick, but nothing more. Not really a solution to a problem, not really any idea of non trivial universality.