Object-recognition dataset stumped the world’s best computer vision models
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Re: Object-recognition dataset stumped the world’s best computer vision models
#2Being able to measure our successes and failures is the first step towards better algorithms :) This is always exciting. I don't think anyone thought that vanilla CNN architectures were all that would ever be needed.
This paper which received an honorable mention this year from NeurIPS conference first attempts to convert the image to a 3d scene before detecting objects. https://arxiv.org/pdf/1906.01618.pdf
Re: Object-recognition dataset stumped the world’s best computer vision models
#3What are the implications for ML used in real-world situations with no training set (or a very limited one) that could have life or death consequences - passenger vehicles, industrial use, military, etc.?
Or is the consensus that it is a matter of time before compute and algorithms make these situations "safe enough," even for edge cases?
Re: Object-recognition dataset stumped the world’s best computer vision models
#4Perhaps the idea of not having a training set means that one should use other datasets such as image net and use transfer learning.
Re: Object-recognition dataset stumped the world’s best computer vision models
#5What are the implications for ML used in real-world situations with no training set (or a very limited one) that could have life or death consequences - passenger vehicles, industrial use, military, etc.? Or is the consensus that it is a matter of time before compute and algorithms make these situations "safe enough," even for edge cases?
It's clearly delusional to think that computer vision will go from soft computing (erroneous) to hard computing in less than a decade, at the rate of current incremental improvements.
We will soon hit an accuracy wall that only breakthrough research will allow to beat.
The problem being: there's too much research exploring the search space in the same direction and not enough foundational research.
Re: Object-recognition dataset stumped the world’s best computer vision models
#6What are the implications for ML used in real-world situations with no training set (or a very limited one) that could have life or death consequences - passenger vehicles, industrial use, military, etc.? Or is the consensus that it is a matter of time before compute and algorithms make these situations "safe enough," even for edge cases?
It's clearly delusional to think that computer vision will go from soft computing (erroneous) to hard computing in less than a decade, at the rate of current incremental improvements. We will soon hit an accuracy wall that only breakthrough research will allow to beat. The problem being: there's too much research exploring the search space in the same direction and not enough foundational research.
I disagree. There is a huge amount of research on adversarially robust classifiers and detectors going on. One can also programmatically test a neural network on real data, synthetically damaged data, fully synthetic data, and adversarial data, and everything in between. You can statistically ensure you get any desired accuracy level on those tests. While that's not a hard proof of anything, it can allow you to be very confident in the nets abilities.
Re: Object-recognition dataset stumped the world’s best computer vision models
#7> But designing bigger versions of ObjectNet, with its added viewing angles and orientations, won’t necessarily lead to better results, the researchers warn.
Curious if there's supporting reasoning for this type of statement? Imho most of these "objects" should still be learnable with vanilla CNNs if you had sufficient data, especially more angles. Starving a vision network of data is an interesting problem, but I don't think it can be used as a blanket statement for all state of the art techniques. And if I'm allowed to make a naive comparison to human intelligence, I don't think lack of viewing angles is a factor.
Re: Object-recognition dataset stumped the world’s best computer vision models
#8It seems like, to be really good the AI needs to construct an internal 3D model of objects so it won't matter which way it's rotated.
It seems to be how the human works. I can rotate an object in my mind and picture it from any angle.
Re: Object-recognition dataset stumped the world’s best computer vision models
#9[deleted]
Re: Object-recognition dataset stumped the world’s best computer vision models
#10It seems like, to be really good the AI needs to construct an internal 3D model of objects so it won't matter which way it's rotated. It seems to be how the human works. I can rotate an object in my mind and picture it from any angle.
You can, but I don't think that's what happens when looking at stuff. When I look at the hammer, I don't rotate a hammer to see what orientation matches what I'm seeing, I see the handle and... metal bit (don't know the term, sorry), realize those look like they belong to a hammer and go "oh yeah, it's an upside down hammer".