5 ms·

Computer Eyesight Gets a Lot More Accurate

61 points by dumitrue 12y ago

liuliu 12y ago
This year's result: http://image-net.org/challenges/LSVRC/2014/results http://image-net.org/challenges/LSVRC/2014/results
- Chronic29 12y ago
  The NYT article is riddled with errors. As pointed out below, the article uses G.P.U. when instead it should be GPU.
brandonmenc 12y ago
Minor editing nitpicks: GPU, not G.P.U. OpenCV, not Open CV c'mon NYT, act like you know.
- thrownaway2424 12y ago
  Open CV is a mistake, but the style guide in force at the Times gives guidance for the use of dots in abbreviations. G.P.U. appears as dictated by their style guide. The Times also inserts dots into C.I.A. and F.B.I. for the same reason.
lifeisstillgood 12y ago
Computer vision is one of those odd areas that I cannot see a nice gentle slope to adoption, but instead is a step change. For example NLP gives us all sorts of add ons to our current interaction with computers (hey let's do sentiment analysis of customer reviews / emails / etc) But there is no obvious slope for computer vision - we need an infrastructure of cameras and bandwidth before it becomes ubiquitous So I struggle to see the profitable intermediate businesses between here and there - and that troubles me.
- nl 12y ago
  I don't think that's entirely true. There are many tasks where the current levels of accuracy are sufficient (eg, registration plate recognition), and as recognition slowly improves more and more tasks become possible. Pete Warden has written extensively on this topic[1]. His "hipster detection" algorithm is quite inaccurate by any conventional measurement, but is accurate enough to be useful. [1] http://petewarden.com/2014/07/31/how-to-get-computer-vision-out-of-the-unimpressive-valley/ http://petewarden.com/2014/07/31/how-to-get-computer-vision-...
- TeMPOraL 12y ago
  There are many intermediate applications. From my own computer vision classes at university I remember examples of jobs when a guy is sitting and looking at a factory line or a machine for 8 hours a day in order to press a button if something goes wrong. This is a kind of work that bores humans out of their minds (thus making them extremely fallible), and that can be done much better with a few cameras and a computer running not-very-supercomplicated computer vision algorithms.
- kastnerkyle 12y ago
  Every smartphone has a camera... and if self-driving cars become a reality there will be a lot of cameras on the road as well. Who needs bandwidth when you can push your models to the local device with a small update? They can just send back batched statistics when a high bandwidth network is available. After all, cars need gas or a charge sometime. It is just a binary patch to change some weights or an architecture layout, which is not so different from updating any other application. Most businesses are covered with cameras as well as hiring people who's only job is to watch those cameras for anomalous activity - I think there are more opportunities than you realize. Farming is another indistry where this technology could be useful.
contingencies 12y ago
Science is but a perversion of itself unless it has as its ultimate goal the betterment of humanity. - Nikola Tesla Does this not nearly amount to "population-scale mass surveillance algorithms"? Do people not feel this is accelerating negative social impacts of technology? Is it merely a coincidence that winning teams include many from countries criticized for their totalitarian social contracts: Hong Kong University of Science and Technology, National University of Singapore, Microsoft Research China, Southeast University (China), Chinese Academy of Sciences? There's also a presence from Holland. Oh, and guess who won the category "with additional training data"? Google. Come on people, we can do better than this! SHAME SHAME SHAME.
- sjtrny 12y ago
  You misunderstand the type of algorithms being developed and tested for this challenge.
  - contingencies 12y ago
    History shows us that virtually all imaging related research is rapidly applied to military and government surveillance efforts. However general the algorithms, the direction these technologies are helping to take society does seem fairly clear at this point. I do not argue there are no good, peaceful uses, merely that major uses are oppressive and that current era actors in this space do not have good records on morality nor a lack of extensive, zero public oversight opportunity to abuse this research to negative social ends.
- kastnerkyle 12y ago
  This can already be done to a large degree... see [1]. That said, this contest is about recognition of items and localization, both of which are key for the future of robotics and have little do with your surveillance state fears. Ultimately, the thing stopping mass surveillance is not a limitation of technology, but of policy. For better or worse, the days of "they don't have the resources to do that" have been replaced by "they aren't allowed to do that". If you have access to the raw packets going to and from every device, and the accelerometer in almost everyone's pocket, identification can be much simpler than doing full face recognition all the time. I seriously doubt the dawn of the surveillance state will be heralded by deep neural networks recognizing faces in the streets - hardware and software backdoors on phones are cheaper and more effective. [1] https://www.facebook.com/publications/546316888800776/ https://www.facebook.com/publications/546316888800776/

61 points by dumitrue 12y ago

This year's result: http://image-net.org/challenges/LSVRC/2014/results http://image-net.org/challenges/LSVRC/2014/results

Chronic29 12y ago
The NYT article is riddled with errors. As pointed out below, the article uses G.P.U. when instead it should be GPU.

brandonmenc 12y ago

Minor editing nitpicks: GPU, not G.P.U. OpenCV, not Open CV c'mon NYT, act like you know.

thrownaway2424 12y ago
Open CV is a mistake, but the style guide in force at the Times gives guidance for the use of dots in abbreviations. G.P.U. appears as dictated by their style guide. The Times also inserts dots into C.I.A. and F.B.I. for the same reason.

lifeisstillgood 12y ago

Computer vision is one of those odd areas that I cannot see a nice gentle slope to adoption, but instead is a step change. For example NLP gives us all sorts of add ons to our current interaction with computers (hey let's do sentiment analysis of customer reviews / emails / etc) But there is no obvious slope for computer vision - we need an infrastructure of cameras and bandwidth before it becomes ubiquitous So I struggle to see the profitable intermediate businesses between here and there - and that troubles me.

nl 12y ago
I don't think that's entirely true. There are many tasks where the current levels of accuracy are sufficient (eg, registration plate recognition), and as recognition slowly improves more and more tasks become possible. Pete Warden has written extensively on this topic[1]. His "hipster detection" algorithm is quite inaccurate by any conventional measurement, but is accurate enough to be useful. [1] http://petewarden.com/2014/07/31/how-to-get-computer-vision-out-of-the-unimpressive-valley/ http://petewarden.com/2014/07/31/how-to-get-computer-vision-...
TeMPOraL 12y ago
There are many intermediate applications. From my own computer vision classes at university I remember examples of jobs when a guy is sitting and looking at a factory line or a machine for 8 hours a day in order to press a button if something goes wrong. This is a kind of work that bores humans out of their minds (thus making them extremely fallible), and that can be done much better with a few cameras and a computer running not-very-supercomplicated computer vision algorithms.
kastnerkyle 12y ago
Every smartphone has a camera... and if self-driving cars become a reality there will be a lot of cameras on the road as well. Who needs bandwidth when you can push your models to the local device with a small update? They can just send back batched statistics when a high bandwidth network is available. After all, cars need gas or a charge sometime. It is just a binary patch to change some weights or an architecture layout, which is not so different from updating any other application. Most businesses are covered with cameras as well as hiring people who's only job is to watch those cameras for anomalous activity - I think there are more opportunities than you realize. Farming is another indistry where this technology could be useful.

contingencies 12y ago

Science is but a perversion of itself unless it has as its ultimate goal the betterment of humanity. - Nikola Tesla Does this not nearly amount to "population-scale mass surveillance algorithms"? Do people not feel this is accelerating negative social impacts of technology? Is it merely a coincidence that winning teams include many from countries criticized for their totalitarian social contracts: Hong Kong University of Science and Technology, National University of Singapore, Microsoft Research China, Southeast University (China), Chinese Academy of Sciences? There's also a presence from Holland. Oh, and guess who won the category "with additional training data"? Google. Come on people, we can do better than this! SHAME SHAME SHAME.

sjtrny 12y ago
You misunderstand the type of algorithms being developed and tested for this challenge.
- contingencies 12y ago
  History shows us that virtually all imaging related research is rapidly applied to military and government surveillance efforts. However general the algorithms, the direction these technologies are helping to take society does seem fairly clear at this point. I do not argue there are no good, peaceful uses, merely that major uses are oppressive and that current era actors in this space do not have good records on morality nor a lack of extensive, zero public oversight opportunity to abuse this research to negative social ends.
kastnerkyle 12y ago
This can already be done to a large degree... see [1]. That said, this contest is about recognition of items and localization, both of which are key for the future of robotics and have little do with your surveillance state fears. Ultimately, the thing stopping mass surveillance is not a limitation of technology, but of policy. For better or worse, the days of "they don't have the resources to do that" have been replaced by "they aren't allowed to do that". If you have access to the raw packets going to and from every device, and the accelerometer in almost everyone's pocket, identification can be much simpler than doing full face recognition all the time. I seriously doubt the dawn of the surveillance state will be heralded by deep neural networks recognizing faces in the streets - hardware and software backdoors on phones are cheaper and more effective. [1] https://www.facebook.com/publications/546316888800776/ https://www.facebook.com/publications/546316888800776/