Vision language models are blind
vlmsareblind.github.io
Vision language models are blind
1–10 of 202 posts
Re: Vision language models are blind
#2This paper examines the limitations of current vision-based language models, such as GPT-4 and Sonnet 3.5, in performing low-level vision tasks. Despite their high scores on numerous multimodal benchmarks, these models often fail on very basic cases. This raises a crucial question: are we evaluating these models accurately?
Re: Vision language models are blind
#3Some of these samples are shocking. How do these models answer chart-based questions, I mean when they can't even count the intersections between two lines?
Re: Vision language models are blind
#4Current approaches of multi-modal models work on embeddings and tokenizations of images, which is the fundamental problem: you are feeding blurry, non-precise data into the model. Yes, they are blind because of exactly this.
An embedding isn't conceptually that much different from feeding a 1024-word description of an image instead of the actual image.
At the moment compute power isn't good enough to feed high-res pixel data into these models, unless we discover a vastly different architecture, which I am also convinced likely exists.
Re: Vision language models are blind
#5Some of these samples are shocking. How do these models answer chart-based questions, I mean when they can't even count the intersections between two lines?
Same way they answer any question: piece together a statistically probable sequence of words to follow the prompt. All they know about an image is a handful of words a classifier might choose to describe it. If those words have nothing to do with the question being asked, they can't nudge the model in the general direction of a correct answer, so it's a crapshoot- even moreso than usual.
Re: Vision language models are blind
#6Current approaches of multi-modal models work on embeddings and tokenizations of images, which is the fundamental problem: you are feeding blurry, non-precise data into the model. Yes, they are blind because of exactly this. An embedding isn't conceptually that much different from feeding a 1024-word description of an image instead of the actual image. At the moment compute power isn't good enough to feed high-res pi…
Doesn't Gemini have a 2 million token limit for exactly this?
Re: Vision language models are blind
#7Wow, that is embarrassingly bad performance for current SOTA models (GPT-4o, Gemini-1.5 Pro, Sonnet-3, Sonnet-3.5), which are advertised and sold as being able to understand images, e.g., for guiding the blind or tutoring children in geometry!
The tasks at which they fail are ridiculously simple for human beings, including, for example:
* counting the number of times two lines intersect;
* detecting whether two circles overlap;
* selecting which letter is being circled in a word;
* counting the number of circles in an Olympic-like logo.
This should be at the top of the front page.
Re: Vision language models are blind
#8Entertaining, 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], which makes the argument that these huge GenAI models are pretty good at things - assuming that it has seen a LOT of that type of data during training (which is true of a great many things). If you make up tasks like this, then yes can be REALLY bad at them, and initial impressions of AGI get harder to justify. But in practice, we aren't just making up tasks to trip up these models. They can be very performant on some tasks and the authors have not presented any real evidence about these two modes.
Re: Vision language models are blind
#9Wow, that is embarrassingly bad performance for current SOTA models (GPT-4o, Gemini-1.5 Pro, Sonnet-3, Sonnet-3.5), which are advertised and sold as being able to understand images, e.g., for guiding the blind or tutoring children in geometry! The tasks at which they fail are ridiculously simple for human beings, including, for example: * counting the number of times two lines intersect; * detecting whether two circl…
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 mistake humans make dealing with these systems.
Humans assume that because a machine learning model has above human accuracy on task X that it implies that it must also have that ability at all the other tasks. While a human with amazing ability at X would indeed have amazing abilities at other tasks, this is not true of machine learning models The opposite thinking is also wrong, that because the model can't do well on task Y it must be unreliable and it's ability on task X is somehow an illusion and not to be trusted.
Re: Vision language models are blind
#10I think while the findings are interesting, the title "Vision language models are blind" is hyperbolic and misleading of the findings. It is true that VLMs have a very different way of sensing and working with image inputs and for low resolutions each image is split up into blocks and then mapped into discrete tokens. They actually really dont have access to finegrained details because this mapping is quite lossy. So, in that sense, these results completely make sense and were not surprising to me but "blind" is a strong implication and the work isnt really able to back that.
Take the first example of the four 2D line plots for instance. I asked Sonnet 3.5 for an answer and retried it 5 times and 2 out of 5 times, it said something like this:
Me:
here's an image with four 2-D line plots. Can you tell me A) how many lines are in each plot? and B) how many times do they intersect each other in each plot?
Write your answers as: Plot 1 (leftmost): ...
Sonnet:
I'll analyze each plot from left to right and provide the requested information:
Plot 1 (leftmost): A) 2 lines (one red, one blue) B) 0 intersections (the lines do not cross)
Plot 2: A) 2 lines (one red, one blue) B) 1 intersection (the lines cross once near the right edge)
Plot 3: A) 2 lines (one red, one blue) B) 1 intersection (the lines cross once near the center)
Plot 4 (rightmost): A) 2 lines (one red, one blue) B) 2 intersections (the lines cross twice, once near the left side and once near the right side)
Each plot consistently shows two lines, one red and one blue, with varying intersection patterns across the four plots.
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Which isn't completely correct (the 3rd plot has 2 intersections) but still pretty good and the other 3 times, it said plot 3 has 1 intersection.