Object-recognition dataset stumped the world’s best computer vision models • Hacker News Board

Re: Object-recognition dataset stumped the world’s best computer vision models

#41

Post by fheinsen » Wed, Dec 11, 2019, 4:52 PM UTC

cs702 wrote: post #34

Datenstrom wrote: post #31

I have been working on this problem for the last year. As it turns out this problem is especially prominent when doing object detection in the real world on a drone platform. Besides a large number of angles/contexts being able to move in 3D adds a large number of scales also, and there is hardly any open training data besides the VisDrone dataset[0] which doesn't even address the scale issue. It is certainly an inte…

I recently saw a paper (which I reposted here on HN a while back) that perhaps could help with your problem. The paper proposes an approach that sort of induces models to learn representations of objects that are good at predicting "the most agreed-upon" rotations of the inputs. I'm not explaining it well. Anyway, a model in the paper achieved SOTA on a change-of-viewpoint dataset with a really tiny number of paramet…

I'm the author of that paper. Happy to answer questions about it here.

Re: Object-recognition dataset stumped the world’s best computer vision models

#42

Post by rstuart4133 » Thu, Dec 12, 2019, 12:43 AM UTC

jobseeker990 wrote: post #8

It 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.

> 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 that doesn't seem to me to be how the mind works. If it looked at it from every angle simultaneously there would be no speed difference in my recognising an object regardless of the orientation.

But in some cases there are a huge speed difference. I can be staring at it for many seconds and then snap! - oh it's upside down. As soon as I realise this my mind immediately adapts and what was unrecognisable is suddenly as plain as day.

The only way I can account for this is when I finally twig the image is upside down I re-route it through a different path in my brain that does a rotation before feeding it to the recogniser. But normally that path is shut off - it's not constantly scanning the input.

I suppose what happens is in most cases some pre-processor uses other clues in the picture to tell me it's rotated from it's normal position and engages the correct path without conscious intervention. That would explain why most of the time you say you don't notice it.

Nonetheless the two mechanisms are very different. A sequential path that rotation -> recognition will be slightly deeper and slightly slower than that does both in the one step, but far smaller. Nonetheless, to looks to me modern designs do attempt to do it in one step, which is to say they attempt to recognise the object in all possible orientations simultaneously.

Re: Object-recognition dataset stumped the world’s best computer vision models

#43

Post by rhizome » Thu, Dec 12, 2019, 2:46 AM UTC

nl wrote: post #22

rhizome wrote: post #20

Earlier quoted context omitted.

The thing about AI/CV and other interpretation simulators is that there is always a quantization of nature in the end result. This is why the Uncanny Valley exists, "Uncanny Valley" being a term that refers to the difference between nature and the model itself, and I'm pretty sure it can't be engineered out of the technology. That is, the simulator can not picture an object from any angle, only an angle greater than…

> This is why the Uncanny Valley exists, "Uncanny Valley" being a term that refers to the difference between nature and the model itself That is incorrect. From the Wikipedia page you references: as the appearance of a robot is made more human, some observers' emotional response to the robot becomes increasingly positive and empathetic, until it reaches a point beyond which the response quickly becomes strong revulsi…

>as the robot's appearance continues to become less distinguishable from a human being, the emotional response becomes positive once again and approaches human-to-human empathy levels

The gap implied by "approaches" is where the problems occur, and how that gap is dealt with is where inherent bias in the process(es) fits.

>Computer vision can process images with a higher resolution than the human eye can distinguish.

The problem is not in the resolution, but the "process" part. Also, "resolution" is not the right word to pivot the situation on, since we'd have to be comparing a digital system with an analog one.

>All models are wrong, but some are useful.

Useful I'll give you, but for life and death decisions? It's not going to help me evade downvotes, but...not so much.