6 ms·

Visual Reasoning Is Coming Soon

122 points by softwaredoug 2y ago

thierrydamiba 2y ago
Excellent write up. The example you used to demonstrate is well done. Such a simple way to describe the current issues.
nkingsy 2y ago
The example of the cat and detective hat shows that even with the latest update, it isn't "editing" the image. The generated cat is younger, with bigger, brighter eyes, more "perfect" ears. I found that when editing images of myself, the result looked weird, like a funky version of me. For the cat, it looks "more attractive" I guess, but for humans (and I'd imagine for a cat looking at the edited cat with a keen eye for cat faces), the features often don't work together when changed slightly.
- porphyra 2y ago
  Chatgpt 4o's advanced image generation seems to have a low-resolution autoregressive part that generates tokens directly, and an image upscaling decoding step that turns the (perhaps 100 px wide) token-image into the actual 1024 px wide final result. The former step is able to almost nail things perfectly, but the latter step will always change things slightly. That's why it is so good at, say, generating large text but still struggles with fine text, and will always introduce subtle variations when you ask it to edit an existing image.
  - BriggyDwiggs42 2y ago
    Has anyone tried putting in a model that selects the editing region prior to the process? Training data would probably be hard, but maybe existing image recognition tech that draws rectangles would be a start.
- ilynd 2y ago
  Genuine question - how would such a model "edit" the image, besides manipulating the binary? I.e. changing pixel values programmatically
CSMastermind 2y ago
What's interesting to me is how many of these advancements are just obvious next steps for these tools. Chain of thought, tree of thought, mixture of experts etc. are things you'd come up with in the first 10 minutes of thinking about improving LLMs. Of course the devil's always in the details and there have been real non-obvious advancements at the same time.
- AstralStorm 2y ago
  The problem always was that the improvements worsened other cases. Mixture of experts tends to lock models up. Chain of thought tends to turn the models loopy. As in circling the drain even when asked not to. Tree of thought has a tendency to make the models unstable by flipping between the branches, and otherwise unpredictable complicating training ...
AIPedant 2y ago
This seems to ignore the mixed record of video generation models: For visual reasoning practice, we can do supervised fine-tuning on sequences similar to the marble example above. For instance, to understand more about the physical world, we can show the model sequential pictures of Slinkys going down stairs, or basketball players shooting 3-pointers, or people hammering birdhouses together.... But where will we get all this training data? For spatial and physical reasoning tasks, we can leverage computer graphics to generate synthetic data. This approach is particularly valuable because simulations provide a controlled environment where we can create scenarios with known outcomes, making it easy to verify the model's predictions. But we'll also need real-world examples. Fortunately, there's an abundance of video content online that we can tap into. While initial datasets might require human annotation, soon models themselves will be able to process videos and their transcripts to extract training examples automatically. Almost every video generator makes constant "folk physics" errors and doesn't understand object permanence. DeepMind's Veo2 is very impressive but still struggles with object permanence and qualitatively nonsensical physics: https://x.com/Norod78/status/1894438169061269750 https://x.com/Norod78/status/1894438169061269750 Humans do not learn these things by pure observation (newborns understand object permanence, I suspect this is the case for all vertebrates). I doubt transformers are capable of learning it as robustly, even if trained on all of YouTube. There will always be "out of distribution" physical nonsense involving mistakes humans (or lizards) would never make, even if they've never seen the specific objects.
- throwanem 2y ago
  > newborns understand object permanence Is that why the peekaboo game is funny for babies? The violated expectation at the soul of the comedy?
  - broof 2y ago
    Yeah I had thought that newborns famously didnt understand object permanence and that it developed sometime during their first year. And that was why peekaboo is fun, you’re essentially popping in and out of existence.
porphyra 2y ago
I think that one reason that humans are so good at understanding images is that our eyes see video rather than still images. Video lets us see "cause and effect" by seeing what happens after something. It also allows us to grasp the 3D structure of things since we will almost always see everything from multiple angles. So long as we just feed a big bunch of stills into training these models, it will struggle to understand how things affect one another.
- throwanem 2y ago
  I have some bad news for you about how every digital video you've ever seen in your life is encoded.
KTibow 2y ago
I've seen some speculate that o3 is already using visual reasoning and that's what made it a breakthrough model.
District5524 2y ago
The first caption of the cat picture may be a bit misleading for those who are not sure of how this works: "The best a traditional LLM can do when asked to give it a detective hat and monocle." The role of the traditional LLM in creating a picture is quite minimal (if there is any LLMs used), it might just tweak a bit the prompt for the diffusion model. It was definitely not the LLM that created the picture: https://platform.openai.com/docs/guides/image-generation https://platform.openai.com/docs/guides/image-generation 4o image generation is surely a bit different, but I don't really have that kind of more precise technical information (there must be indeed a specialized transformer model used, linking tokens to pixels, https://openai.com/index/introducing-4o-image-generation/ https://openai.com/index/introducing-4o-image-generation/)
uaas 2y ago
> Rather watch than read? Hey, I get it - sometimes you just want to kick back and watch! Check out this quick video where I walk through everything in this post Hm, no, I’ve never had this thought.
- esperent 2y ago
  Me neither. But some people are dyslexic or non native speakers (it's common to have listening comprehension better than reading comprehension when learning a language) and have to put more effort into reading than I do, and some people genuinely just prefer video. I think it's decent of the author to provide a video for these people.
- roguecoder 2y ago
  Pivot To Video will never die.
rel_ic 2y ago
The inconsistency of an optimistic blog post ending with a picture of a terminator robot makes me think this author isn't taking themself seriously enough. Or - the author is the terminator robot?
- deleted 2y ago
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deleted 2y ago
[deleted]
Tiberium 2y ago
It's sad that they used 4o's image generation feature for the cat example which does some diffusion or something else, results in the whole image changing. They should've instead used Gemini 2.0 Flash's image generation feature (or at least mentioned it!), which, even if far lower quality and resolution (max of 1024x1024, but Gemini will try to match the res of the original image, so you can get something like 681x1024), is much much better at leaving the untouched parts of the image actually "untouched". Here's the best out of a few attempts for a really similar prompt, more detailed since Flash is a much smaller model "Give the cat a detective hat and a monocle over his right eye, properly integrate them into the photo.". You can see how the rest of the image is practically untouched to the naked human eye: https://ibb.co/zVgDbqV3 https://ibb.co/zVgDbqV3 Honestly Google has been really good at catching up in the LLM race, and their modern models like 2.0 Flash, 2.5 Pro are one of (or the) best in their respective areas. I hope that they'll scale up their image generation feature to base it on 2.5 Pro (or maybe 3 Pro by the time they do it) for higher quality and prompt adherence. If you want, you can give 2.0 Flash image gen a try for free (with generous limits) on https://aistudio.google.com/prompts/new_chat https://aistudio.google.com/prompts/new_chat, just select it in the model selector on the right.
- blixt 2y ago
  I'm not sure I see the behavior in the Gemini 2.0 Flash model's image output as a strength. It seems to me it has multiple output modes, one indeed being masked edits. But it also seems to have convolutional matrix edits (e.g. "make this image grayscale" looks practically like it's applying a Photoshop filter) and true latent space edits ("show me this scene 1 minute later" or "move the camera so it is above this scene, pointing down"). And it almost seems to me these are actually distinct modes, which seems like it's been a bit too hand engineered. On the other hand, OpenAI's model, while it does seem to have some upscaling magic happening (which makes the outputs look a lot nicer than the ones from Gemini FWIW), also seems to perform all its edits entirely in latent space (hence it's easy to see things degrade at a conceptual level such as texture, rotation, position, etc.) But this is a sign that its latent space mode is solid enough to always use, while with Gemini 2.0 Flash I get the feeling when it is used, it's just not performing as well.
- esperent 2y ago
budmichstelk 2y ago
[dead]
anxoo 2y ago
"I set a plate on a table, and glass next to it. I set a marble on the plate. Then I pick up the marble, drop it in the glass. Then I turn the glass upside down and set it on the plate. Then, I pick up the glass and put it in the microwave. Where is the marble?" the author claims that visual reasoning will help the model solve this problem, noting that gpt-4o got the question right after making a mistake in the beginning of the response. i asked gpt-4o, claude 3.7, and gemini 2.5 pro experimental, who all answered 100% correctly. the author also demonstrates trying to do "visual reasoning" with gpt-4o, notes that the model got it wrong, then handwaves it away by saying the model wasn't trained for visual reasoning. "visual reasoning" is a tweet-worthy thought that the author completely fails to justify

122 points by softwaredoug 2y ago

Excellent write up. The example you used to demonstrate is well done. Such a simple way to describe the current issues.

The example of the cat and detective hat shows that even with the latest update, it isn't "editing" the image. The generated cat is younger, with bigger, brighter eyes, more "perfect" ears. I found that when editing images of myself, the result looked weird, like a funky version of me. For the cat, it looks "more attractive" I guess, but for humans (and I'd imagine for a cat looking at the edited cat with a keen eye for cat faces), the features often don't work together when changed slightly.

porphyra 2y ago
Chatgpt 4o's advanced image generation seems to have a low-resolution autoregressive part that generates tokens directly, and an image upscaling decoding step that turns the (perhaps 100 px wide) token-image into the actual 1024 px wide final result. The former step is able to almost nail things perfectly, but the latter step will always change things slightly. That's why it is so good at, say, generating large text but still struggles with fine text, and will always introduce subtle variations when you ask it to edit an existing image.
- BriggyDwiggs42 2y ago
  Has anyone tried putting in a model that selects the editing region prior to the process? Training data would probably be hard, but maybe existing image recognition tech that draws rectangles would be a start.
ilynd 2y ago
Genuine question - how would such a model "edit" the image, besides manipulating the binary? I.e. changing pixel values programmatically

CSMastermind 2y ago

What's interesting to me is how many of these advancements are just obvious next steps for these tools. Chain of thought, tree of thought, mixture of experts etc. are things you'd come up with in the first 10 minutes of thinking about improving LLMs. Of course the devil's always in the details and there have been real non-obvious advancements at the same time.

AstralStorm 2y ago
The problem always was that the improvements worsened other cases. Mixture of experts tends to lock models up. Chain of thought tends to turn the models loopy. As in circling the drain even when asked not to. Tree of thought has a tendency to make the models unstable by flipping between the branches, and otherwise unpredictable complicating training ...

AIPedant 2y ago

This seems to ignore the mixed record of video generation models: For visual reasoning practice, we can do supervised fine-tuning on sequences similar to the marble example above. For instance, to understand more about the physical world, we can show the model sequential pictures of Slinkys going down stairs, or basketball players shooting 3-pointers, or people hammering birdhouses together.... But where will we get all this training data? For spatial and physical reasoning tasks, we can leverage computer graphics to generate synthetic data. This approach is particularly valuable because simulations provide a controlled environment where we can create scenarios with known outcomes, making it easy to verify the model's predictions. But we'll also need real-world examples. Fortunately, there's an abundance of video content online that we can tap into. While initial datasets might require human annotation, soon models themselves will be able to process videos and their transcripts to extract training examples automatically. Almost every video generator makes constant "folk physics" errors and doesn't understand object permanence. DeepMind's Veo2 is very impressive but still struggles with object permanence and qualitatively nonsensical physics: https://x.com/Norod78/status/1894438169061269750 https://x.com/Norod78/status/1894438169061269750 Humans do not learn these things by pure observation (newborns understand object permanence, I suspect this is the case for all vertebrates). I doubt transformers are capable of learning it as robustly, even if trained on all of YouTube. There will always be "out of distribution" physical nonsense involving mistakes humans (or lizards) would never make, even if they've never seen the specific objects.

throwanem 2y ago
> newborns understand object permanence Is that why the peekaboo game is funny for babies? The violated expectation at the soul of the comedy?
- broof 2y ago
  Yeah I had thought that newborns famously didnt understand object permanence and that it developed sometime during their first year. And that was why peekaboo is fun, you’re essentially popping in and out of existence.

porphyra 2y ago

I think that one reason that humans are so good at understanding images is that our eyes see video rather than still images. Video lets us see "cause and effect" by seeing what happens after something. It also allows us to grasp the 3D structure of things since we will almost always see everything from multiple angles. So long as we just feed a big bunch of stills into training these models, it will struggle to understand how things affect one another.

throwanem 2y ago
I have some bad news for you about how every digital video you've ever seen in your life is encoded.

KTibow 2y ago

I've seen some speculate that o3 is already using visual reasoning and that's what made it a breakthrough model.

District5524 2y ago

The first caption of the cat picture may be a bit misleading for those who are not sure of how this works: "The best a traditional LLM can do when asked to give it a detective hat and monocle." The role of the traditional LLM in creating a picture is quite minimal (if there is any LLMs used), it might just tweak a bit the prompt for the diffusion model. It was definitely not the LLM that created the picture: https://platform.openai.com/docs/guides/image-generation https://platform.openai.com/docs/guides/image-generation 4o image generation is surely a bit different, but I don't really have that kind of more precise technical information (there must be indeed a specialized transformer model used, linking tokens to pixels, https://openai.com/index/introducing-4o-image-generation/ https://openai.com/index/introducing-4o-image-generation/)

uaas 2y ago

> Rather watch than read? Hey, I get it - sometimes you just want to kick back and watch! Check out this quick video where I walk through everything in this post Hm, no, I’ve never had this thought.

esperent 2y ago
Me neither. But some people are dyslexic or non native speakers (it's common to have listening comprehension better than reading comprehension when learning a language) and have to put more effort into reading than I do, and some people genuinely just prefer video. I think it's decent of the author to provide a video for these people.
roguecoder 2y ago
Pivot To Video will never die.

rel_ic 2y ago

The inconsistency of an optimistic blog post ending with a picture of a terminator robot makes me think this author isn't taking themself seriously enough. Or - the author is the terminator robot?

deleted 2y ago
[deleted]

deleted 2y ago

[deleted]

Tiberium 2y ago

It's sad that they used 4o's image generation feature for the cat example which does some diffusion or something else, results in the whole image changing. They should've instead used Gemini 2.0 Flash's image generation feature (or at least mentioned it!), which, even if far lower quality and resolution (max of 1024x1024, but Gemini will try to match the res of the original image, so you can get something like 681x1024), is much much better at leaving the untouched parts of the image actually "untouched". Here's the best out of a few attempts for a really similar prompt, more detailed since Flash is a much smaller model "Give the cat a detective hat and a monocle over his right eye, properly integrate them into the photo.". You can see how the rest of the image is practically untouched to the naked human eye: https://ibb.co/zVgDbqV3 https://ibb.co/zVgDbqV3 Honestly Google has been really good at catching up in the LLM race, and their modern models like 2.0 Flash, 2.5 Pro are one of (or the) best in their respective areas. I hope that they'll scale up their image generation feature to base it on 2.5 Pro (or maybe 3 Pro by the time they do it) for higher quality and prompt adherence. If you want, you can give 2.0 Flash image gen a try for free (with generous limits) on https://aistudio.google.com/prompts/new_chat https://aistudio.google.com/prompts/new_chat, just select it in the model selector on the right.

blixt 2y ago
I'm not sure I see the behavior in the Gemini 2.0 Flash model's image output as a strength. It seems to me it has multiple output modes, one indeed being masked edits. But it also seems to have convolutional matrix edits (e.g. "make this image grayscale" looks practically like it's applying a Photoshop filter) and true latent space edits ("show me this scene 1 minute later" or "move the camera so it is above this scene, pointing down"). And it almost seems to me these are actually distinct modes, which seems like it's been a bit too hand engineered. On the other hand, OpenAI's model, while it does seem to have some upscaling magic happening (which makes the outputs look a lot nicer than the ones from Gemini FWIW), also seems to perform all its edits entirely in latent space (hence it's easy to see things degrade at a conceptual level such as texture, rotation, position, etc.) But this is a sign that its latent space mode is solid enough to always use, while with Gemini 2.0 Flash I get the feeling when it is used, it's just not performing as well.
esperent 2y ago

budmichstelk 2y ago

[dead]

anxoo 2y ago

"I set a plate on a table, and glass next to it. I set a marble on the plate. Then I pick up the marble, drop it in the glass. Then I turn the glass upside down and set it on the plate. Then, I pick up the glass and put it in the microwave. Where is the marble?" the author claims that visual reasoning will help the model solve this problem, noting that gpt-4o got the question right after making a mistake in the beginning of the response. i asked gpt-4o, claude 3.7, and gemini 2.5 pro experimental, who all answered 100% correctly. the author also demonstrates trying to do "visual reasoning" with gpt-4o, notes that the model got it wrong, then handwaves it away by saying the model wasn't trained for visual reasoning. "visual reasoning" is a tweet-worthy thought that the author completely fails to justify