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Vision language models are blind

vlmsareblind.github.io

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Re: Vision language models are blind

#91

Post by Melomololotolo »

SomaticPirate wrote: post #18
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…

There are quite a few "ai apologists" in the comments but I think the title is fair when these models are marketed towards low vision people ("Be my eyes" https://www.youtube.com/watch?v=Zq710AKC1gg ) as the equivalent to human vision. These models are implied to be human level equivalents when they are not. This paper demonstrates that there are still some major gaps where simple problems confound the models in unex…

Ah yes the blind person who constantly needs to know if two lines intersect.

Let's just ignore what a blind person normally needs to know.

You know what blind people ask? Sometimes there daily routine is broken because there is some type of construction and models can tell you this.

Sometimes they need to read a basic sign and models can do this.

Those models help people already and they will continue to get better.

I'm not sure if I'm more frustrated how condescending the authors are or your ignorance.

Valid criticism doesn't need to be shitty

Re: Vision language models are blind

#92

Post by viraptor »

I love some of the interpretations there. For example "Fig. 10: Only Sonnet-3.5 can count the squares in a majority of the images.", when that model simply returns "4" for every question and happens to be right.

Re: Vision language models are blind

#93

Post by Melomololotolo »

sweezyjeezy wrote: post #23
SomaticPirate wrote: post #18

Earlier quoted context omitted.

There are quite a few "ai apologists" in the comments but I think the title is fair when these models are marketed towards low vision people ("Be my eyes" https://www.youtube.com/watch?v=Zq710AKC1gg ) as the equivalent to human vision. These models are implied to be human level equivalents when they are not. This paper demonstrates that there are still some major gaps where simple problems confound the models in unex…

The paper I linked should hopefully mark me out as far from an AI apologist, it's actually really bad news for GenAI if correct. All I mean to say is the clickbait conclusion and the evidence do not match up.

We have started the ara of ai.

It really doesn't matter how good current llms are.

They have been good enough to start this ara.

And no it's not and never has been just llms. Look what Nvidia is doing with ml.

Whisper huge advantage, segment anything again huge. Alpha fold 2 again huge.

All the robot announcements -> huge

I doubt we will reach agi just through llms. We will reach agi through multi modal, mix of experts, some kind of feedback loop, etc.

But the stone started to roll.

And you know I prefer to hear about ai advantages for the next 10-30 years. That's a lot better than the crypto shit we had the last 5 years.

Re: Vision language models are blind

#94

Post by benreesman »

SomaticPirate wrote: post #18
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…

There are quite a few "ai apologists" in the comments but I think the title is fair when these models are marketed towards low vision people ("Be my eyes" https://www.youtube.com/watch?v=Zq710AKC1gg ) as the equivalent to human vision. These models are implied to be human level equivalents when they are not. This paper demonstrates that there are still some major gaps where simple problems confound the models in unex…

If we're throwing "citation needed" tags on stuff, how about the first sentence?

"Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 Pro are powering countless image-text processing applications"

I don't know how many a "countless" is, but I think we've gotten really sloppy in terms of what counts for LLMs as a demonstrated, durable win in a concrete task attached to well-measured outcomes and holding up over even modest periods of time.

This stuff is really promising and lots of builders are making lots of nifty things, so if that counts as an application then maybe we're at countless, but in the enterprise and in government and in refereed academic literature we seem to be at the proof-of-concept phase. Impressive chat bots as a use case are pretty dialed in, enough people claim that they help with coding that I tend to believe it's a real thing (I never seem to come out ahead of going directly to the source, StackOverflow).

The amount of breathless press on this seems "countless", so maybe I missed the totally rigorous case study on how X company became Y percent more profitable by doing Z thing with LLMs (or similar), and if so I'd be grateful for citations, but neither Google nor any of the big models seem to know about it.

Re: Vision language models are blind

#95

Post by jazzyjackson »

empath75 wrote: post #73
jazzyjackson wrote: post #63

Earlier quoted context omitted.

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 indep…

It really shouldn't be surprising that these models fail to do anything that _they weren't trained to do_. It's trivially easy to train a model to count stuff. The wild thing about transformer based models is that their capabilities are _way_ beyond what you'd expect from token prediction. Figuring out what their limitations actually are is interesting because nobody fully knows what their limitations are.

I agree that these open ended transformers are way more interesting and impressive than a purpose built count-the-polygons model, but if the model doesn't generalize well enough to figure out how to count the polygons, I can't be convinced that they'll perform usefully on a more sophisticated task.

I agree this research is really interesting, but I didn't have an a priori expectation of what token prediction could accomplish, so my reaction to a lot of the claims and counterclaims of this new tech is that it's good at fooling people and giving plausible but baseless results. It makes for good research but dangerous in the hands of a market attempting to exploit it.

Re: Vision language models are blind

#97

Post by JeremyHerrman »

Eisenstein wrote: post #85
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…

Vision models use CLiP or something similar, which has no conception of anything specific in the image. It sees embeddings which correlate similarly to text embeddings. Take an image then describe it 'there are birds sitting on a power line in front of a blue sky with some clouds', get the embeddings from that and the embeddings from that picture and line them up. If you ask if there are birds in it, it would know, b…

VLMs like PaliGemma and Florence-2 support object detection and segmentation, so it's becoming more common to have YOLO like capabilities built into VLMs.

Another benefit of VLMs which support object detection is that they are open vocabulary, meaning you don't have to define the classes ahead of time. Additionally fine tuning tends to keep the previous detection capabilities instead of erasing all previous classes like fine tuning YOLO.

Re: Vision language models are blind

#98

Post by orbital-decay »

godelski wrote: post #87
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…

> Did they try to probe that hypothesis at all? I think this is a communication issue and you're being a bit myopic in your interpretation. It is clearly an analogy meant for communication and is not an actual hypothesis. Sure, they could have used a better analogy and they could have done other tests, but the paper still counters quite common claims (from researchers) about VLMs. > I could (well actually I can't) sh…

> I think this is a communication issue and you're being a bit myopic in your interpretation. It is clearly an analogy meant for communication and is not an actual hypothesis.

I don't know, words have meanings. If that's a communication issue, it's on part of the authors. To me, this wording in a what is supposed to be a research paper abstract clearly suggests the insufficient resolution as the cause. How else should I interpret it?

> The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like that of a person with myopia seeing fine details as blurry

And indeed, increasing the resolution is expensive, and the best VLMs have something like 1000x1000. But the low resolution is clearly not the issue here, and the authors don't actually talk about it in the paper.

>I find it hard to believe that there is no example you can give.

I'm not the person you're answering to, but I actually lazily tried two of authors' examples in a less performant VLM (CogVLM), and was surprised it passed those, making me wonder whether I can trust their conclusions until I reproduce their results. LLMs and VLMs have all kinds of weird failure modes, it's not a secret they fail at some trivial tasks and their behavior is still not well understood. But working with these models and narrowing it down is notoriously like trying to nail a jelly to the wall. If I was able to do this in a cursory check, what else is there? More than one research paper in this area is wrong from the start.

Re: Vision language models are blind

#99

Post by [deleted] »

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.

[deleted]

Re: Vision language models are blind

#100

Post by warkdarrior »

hi_dang_ wrote: post #32

I was hoping that someone in the comments talking the paper down would have published a paper or have had relevant publications of their own to point to. You know, meet the lads halfway sort of thing. So what I’m left with to judge instead is anonymous online commenters vs. the publication of 2 prestigious universities. Whose word do I take on this? Decisions, decisions. You can swap LM out with Web3 out with NFT out…

> I’m left with [...] is anonymous online commenters vs. the publication of 2 prestigious universities. Whose word do I take on this?

Maybe you need to judge the contents of those online comments and the contents of the publication, instead of relying on argument from authority.

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