Very interesting read. I first learned this method from a random reddit post a while ago and very happy to see a systematic study on this (wish I would save the original post somewhere to reference to!).
Yup, people have been using local video models like Wan2.2 to generate stills, finding that for some things like human anatomy, it can outperform image generation models. Very cool how moving training data helps build spatial understanding that is applicable even to still images.
What is specific about this model? These categories aren't what defines intelligence in animal life. Segmentation is a post-hoc assertion into visual science, not necessarily an inside-out process inherent to perception.
These models aren't the path, they're cheap workarounds that exclude the senses.
I don't understand what point you're making exactly. What categories do you mean? What do you mean by segmentation not necessarily being "an inside-out process inherent to perception"?
The criteria for learning in this model has nothing to do with biological intelligence.
Segmentation is a cog-sci hold-over of vision science and Marr and isn't how brain's perceive scenes/objects/events.
There is a relatovely new approach to perception that ML has ignored that's integrative, coordinated, holistic. These new approaches, affective, coordinated-dynamical, ecological (optic-flow) are the likely routes to consciousness.
What ML does with images like these are retrofit, they're imposed ad hoc on imagery as a pretend form of intelligence.
The senses can't be excluded from consciousness or intelligence, otherwise the notion of intelligence is reduced from an arbitrary set of tests/criteria.
Robotics and trained analogies, arbitrary ideas of affordance (which are not affordances) are definitely interesting, but they're not paths to intel. They're paths to homogenization posing as intelligence.
This is the classic robotics idea of computer vision backing itself into a corner.
I think it's someone playing a prank, based on their comments history here (almost everything cryptic and full of non sequiturs), and also... look at their username: "mallowdram". Say it out loud ;)
Very interesting read. I first learned this method from a random reddit post a while ago and very happy to see a systematic study on this (wish I would save the original post somewhere to reference to!).
Is it possible to use a model trained on video to output single frames?
Yup, people have been using local video models like Wan2.2 to generate stills, finding that for some things like human anatomy, it can outperform image generation models. Very cool how moving training data helps build spatial understanding that is applicable even to still images.
[0] https://www.reddit.com/r/StableDiffusion/comments/1mcm7qm/wa...
> for some things like human anatomy
Pursuit of prurient interests has pioneered so many technologies: photography, videography, AI
What is specific about this model? These categories aren't what defines intelligence in animal life. Segmentation is a post-hoc assertion into visual science, not necessarily an inside-out process inherent to perception.
These models aren't the path, they're cheap workarounds that exclude the senses.
I don't understand what point you're making exactly. What categories do you mean? What do you mean by segmentation not necessarily being "an inside-out process inherent to perception"?
The criteria for learning in this model has nothing to do with biological intelligence.
Segmentation is a cog-sci hold-over of vision science and Marr and isn't how brain's perceive scenes/objects/events.
There is a relatovely new approach to perception that ML has ignored that's integrative, coordinated, holistic. These new approaches, affective, coordinated-dynamical, ecological (optic-flow) are the likely routes to consciousness.
What ML does with images like these are retrofit, they're imposed ad hoc on imagery as a pretend form of intelligence.
Inside-Out is a reversal of the stimuli model and a reversal of the cog-sci cognition model.
https://academic.oup.com/book/35081
The senses can't be excluded from consciousness or intelligence, otherwise the notion of intelligence is reduced from an arbitrary set of tests/criteria.
Robotics and trained analogies, arbitrary ideas of affordance (which are not affordances) are definitely interesting, but they're not paths to intel. They're paths to homogenization posing as intelligence.
This is the classic robotics idea of computer vision backing itself into a corner.
https://docs.google.com/presentation/d/1Wkno8pKzWiav1a7c8IOr...
[flagged]
You again? Learn some manners engineer.
[flagged]
[flagged]
And, it will be easy. You've made junk tech from pseudoscience, classic houses of cards.
It's either a bot or a random internet schizophrenic.
I think it's someone playing a prank, based on their comments history here (almost everything cryptic and full of non sequiturs), and also... look at their username: "mallowdram". Say it out loud ;)
[flagged]
[flagged]
To train an AI to solve problems, you train it extrapolate the future from a starting state of having a problem and the intention to solve the it.
So much falls out of that reframing.
Training is first done as a general predictive model: situation => result
Then it's fine-tuned on: situation + intent => action => result
maybe we really are headed to The One Model that can do it all
Multimodal models do it already.
This is incredible.