Yeah but you're assuming that you know how their creating this data. Just throwing an option out there - what if they created a latent space of 3D people and iteratively expanded it with GANs and 2D real image datasets. That would generalize.
Just a thought, not sure what's really going on there, I just know that they probably have something interesting they're cooking up!
"...original dataset without having access to a critical basis set of the original?"
I think that they're not trying to copy existing datasets but are trying to generate new datasets that solve various computer vision use-cases. Looks lke they're using 3D photorealistic models and environments to then generate 2D data. It is a cool idea, if they had the ability to synthesize a large amount of 3D people and objects and insert them into 3D environment in ways that made sense and then run motion simulation, they could hypothetically create an incredible amount of high-quality data. Sounds pretty hard to do honestly...
I think Monte Carlo is used for something very different than computer vision / machine learning. Monte Carlo is usually used to estimate an average result given many dependent variables and a simplified model of the problem. So if I want to estimate how far my paper airplane will fly and I have a simulator, I would vary the paper thickness, folds and wind. Each time I would run the simulator, get a result and then I can estimate the average distance the paper airplane would go! (actually sounds like a fun project lol). Anyway this is just different.
Simulation is good for edge cases because you can simulate them disproportionally to their prevalence in the real world. So let's say that we're in a smart store and we want to recognize when an elderly person falls on the floor to send human help to the correct location. This happens maybe one in 5 year in a given store. If we were to gather data we may get 10 examples. If they can simulate this, they could simulate 100k elderly people falling and then train models to recognize it! Kind of crazy really.
I don't see few shot, one-shot or no-shot getting anywhere close to standard supervised learning for anything practical. It really doesn't make sense in production settings at all.
You have a function that you want to learn, let's say mapping between an RGB image to a segmentation map. For most applications you're never really in a situation where a production product is dealing with visual scenes/objects it has never seen before. In a factory, in smart stores, in cars, AR scenarios like I just don't see it happening. And then if this case is removed, I'm thinking, ok so when can I get good enough results from a tiny dataset? Machine Learning isn't magic, you're trying to learn a function with 100 million parameters using a dataset, I just don't see the math working out. More data provides better results, it inserts more information to create a more relevant mapping function from input to output.
Third point is great! As long as the models are somehow based on real-world scans I think a lot of good can come from this. The funny thing is, there is so much bias in networks trained today precisely because the data captured is usually small and captured from a specific area/population/setting. If you had a great synthetic data generation engine you could at least generate equal representation of gender, age groups, ethnicities, ... etc.
Just a thought, not sure what's really going on there, I just know that they probably have something interesting they're cooking up!
This is a really crazy vision