Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Vision language models are blind

vlmsareblind.github.io

Post reply on HN
Print view View original

61–70 of 202 posts

Re: Vision language models are blind

#61

Post by blackmesaind »

FrenchDevRemote wrote: post #47
sweezyjeezy wrote: post #8

Entertaining, but I think the conclusion is way off. > their vision is, at best, like that of a person with myopia seeing fine details as blurry is a crazy thing to write in an abstract. Did they try to probe that hypothesis at all? I could (well actually I can't) share some examples from my job of GPT-4v doing some pretty difficult fine-grained visual tasks that invalidate this. Personally, I rate this paper [1], wh…

> their vision is, at best, like that of a person with myopia seeing fine details as blurry It's not that far from reality, most models sees images in very low resolution/limited colors, so not so far from this description

My thoughts as well. I too would have trouble with the overlapping lines tests if all the images underwent convolution.

Re: Vision language models are blind

#62

Post by simonw »

cs702 wrote: post #13
tensor wrote: post #9

Earlier quoted context omitted.

I don't see how this is "embarrassing" in the slightest. These models are not human brains, and the fact that people equate them with human brains is an embarrassing failure of the humans more than anything about the models. It's entirely unsurprising that there are numerous cases that these models can't handle that are "obvious to humans." Machine learning has had this property since its invention and it's a classic…

It is embarrassingly, shockingly bad, because these models are advertised and sold as being capable of understanding images. Evidently, all these models still fall short.

I see this complaint about LLMs all the time - that they're advertised as being infallible but fail the moment you give them a simple logic puzzle or ask for a citation.

And yet... every interface to every LLM has a "ChatGPT can make mistakes. Check important info." style disclaimer.

The hype around this stuff may be deafening, but it's often not entirely the direct fault of the model vendors themselves, who even put out lengthy papers describing their many flaws.

Re: Vision language models are blind

#63

Post by jazzyjackson »

JeremyHerrman wrote: post #16

VLMs so far have never been good at counting objects or spatial relationships (e.g. the coffee is to the right of the microwave). There are ways to help the VLM out - Set of Marks [0] from Microsoft being the most prominent, which uses segmentation to outline and label sections of the image before sending to the VLM. Providing "speakable" labels to regions helps ground the visual abilities of VLMs and is why in this…

I didn't know counting objects was a problem. That's pretty ironic because the very first implementation of a neural net (AFAIK) is the numa-rete artificial retina developed at the Biological Computer Lab [0] circa 1960. It was a parallel analog computer composed of "nuerons" each with a photocell that could be arranged in a grid and count "the number of objects independent of their size, location and form, and independent of strength of illumination" [1] - this paper may be of interest to those in the field, "Perception of Form in Biological and Man Made Systems" Heinz Von Foerster 1962

[0] https://distributedmuseum.illinois.edu/exhibit/biological_co...

[1] https://sites.evergreen.edu/arunchandra/wp-content/uploads/s...

Re: Vision language models are blind

#65

Post by jetrink »

I had a remarkable experience with GPT-4o yesterday. Our garage door started to fall down recently, so I inspected it and found that our landlord had installed the wire rope clips incorrectly, leading to the torsion cables losing tension. I didn't know what that piece of hardware was called, so I asked ChatGPT and it identified the part as I expected it to. As a test, I asked if there was anything notable about the photo. ChatGPT correctly identified that the cables were installed backwards, with the side of the cable that was (previously) under tension on top of the slack end, instead of sandwiched securely in the middle. To diagnose that requires tracing the cable through space and inferring which end is under tension from the geometry, though I can't rule out an educated guess.

What was really remarkable though was that it failed to notice that one of the two nuts was obviously missing, even after I told it there was a second problem with the installation.

Screenshot: https://imgur.com/a/QqCNzOM

Re: Vision language models are blind

#67

Post by jazzyjackson »

simonw wrote: post #62
cs702 wrote: post #13

Earlier quoted context omitted.

It is embarrassingly, shockingly bad, because these models are advertised and sold as being capable of understanding images. Evidently, all these models still fall short.

I see this complaint about LLMs all the time - that they're advertised as being infallible but fail the moment you give them a simple logic puzzle or ask for a citation. And yet... every interface to every LLM has a "ChatGPT can make mistakes. Check important info." style disclaimer. The hype around this stuff may be deafening, but it's often not entirely the direct fault of the model vendors themselves, who even put…

There's evidently a large gap between what researchers publish, the disclaimers a vendor makes, and what gets broadcast on CNBC, no surprise there.

Re: Vision language models are blind

#68

Post by efskap »

akavi wrote: post #45

Speaking as someone with only a tenuous grasp of how VLMs work, this naïvely feels like a place where the "embodiement" folks might have a point: Humans have the ability to "refine" their perception of an image iteratively, focusing in on areas of interest, while VLMs have to process the entire image at the same level of fidelity. I'm curious if there'd be a way to emulate this (have the visual tokens be low fidelity…

Isn't this the attention mechanism, the reason we're using transformers for these things? Maybe not greater resolution per se, but focusing on a region with greater neural connectivity

Re: Vision language models are blind

#69

Post by sfink »

jetrink wrote: post #65

I had a remarkable experience with GPT-4o yesterday. Our garage door started to fall down recently, so I inspected it and found that our landlord had installed the wire rope clips incorrectly, leading to the torsion cables losing tension. I didn't know what that piece of hardware was called, so I asked ChatGPT and it identified the part as I expected it to. As a test, I asked if there was anything notable about the p…

A human would need to trace the cable. An LLM may just be responding based on (1) the fact that you're asking about the clip in the first place, and that commonly happens when there's something wrong; and (2) that this is a very common failure mode. This is supported by it bringing up the "never saddle a dead horse" mnemonic, which suggests the issue is common.

After you fix it, you should try asking the same questions!

Re: Vision language models are blind

#70

Post by vikramkr »

FrenchDevRemote wrote: post #47
sweezyjeezy wrote: post #8

Entertaining, but I think the conclusion is way off. > their vision is, at best, like that of a person with myopia seeing fine details as blurry is a crazy thing to write in an abstract. Did they try to probe that hypothesis at all? I could (well actually I can't) share some examples from my job of GPT-4v doing some pretty difficult fine-grained visual tasks that invalidate this. Personally, I rate this paper [1], wh…

> their vision is, at best, like that of a person with myopia seeing fine details as blurry It's not that far from reality, most models sees images in very low resolution/limited colors, so not so far from this description

They didn't test that claim at all though. Vision isn't some sort of 1D sliding scale with every vision condition lying along one axis.

First of all myopia isn't 'seeing fine details as blurry' - it's nearsightedness - and whatever else this post tested it definitely didn't test depth perception.

And second - inability to see fine details is a distinct/different thing from not being able to count intersections and the other things tested here. That hypothesis, if valid, would imply that improving the resolution of the image that the model can process would improve its performance on these tasks even if reasoning abilities were the same. That - does not make sense. Plenty of the details in these images that these models are tripping up on are perfectly distinguishable at low resolutions. Counting rows and columns of blank grids is not going to improve with more resolution.

I mean, I'd argue that the phrasing of the hypothesis ("At best, like that of a person with myopia") doesn't make sense at all. I don't think a person with myopia would have any trouble with these tasks if you zoomed into the relevant area, or held the image close. I have a very strong feeling that these models would continue to suffer on these tasks if you zoomed in. Nearsighted != unable to count squares.

Post reply on HN