Or the standard given a bunch of tags (from the instance segmentation) and find your ranked list of preferences. https://librecommender.readthedocs.io/en/latest/ has a list of 5-10 standard recommender algorithms.
> analyzing your camera roll. Using deep learning, it then surfaces a few highly relevant profiles each day, which should lead to more compatible matches.
How would this work? It identifies just objects / places that imply interests?
Sounds like a blackbox that assumes I want to ski ... because some pics look "like" skiing or something. Or tries to say connect someone with their ex or people who look like their ex ...
Granted I suspect this is just AI for their sake / identifying faces and collecting mass data ...
> Sounds like a blackbox that assumes I want to ski ... because some pics look "like" skiing or something. Or tries to say connect someone with their ex or people who look like their ex ...
Kinda. But collaborative filtering by the textbook definition tries to find the people who have those embedding and then match those nodes with other similar nodes.
So because you are a person who likes skiing, the system tries to find other people's existing preferences that likes skiing and match those to the items to show you.
I'd assume they'll use something like https://blog.roboflow.com/rf-detr-segmentation-preview/ and then use the "classes" as embeddings for your profile to do a similiarity match.
Or the standard given a bunch of tags (from the instance segmentation) and find your ranked list of preferences. https://librecommender.readthedocs.io/en/latest/ has a list of 5-10 standard recommender algorithms.
Or use autogluon https://auto.gluon.ai/stable/tutorials/tabular/tabular-multi... where they take Meta's large repository of annotated-images (DINOv2) and use them to classify then do the recommendation system on tabular (database tables).
This stuff has been standard for years now.
> analyzing your camera roll. Using deep learning, it then surfaces a few highly relevant profiles each day, which should lead to more compatible matches.
How would this work? It identifies just objects / places that imply interests?
Sounds like a blackbox that assumes I want to ski ... because some pics look "like" skiing or something. Or tries to say connect someone with their ex or people who look like their ex ...
Granted I suspect this is just AI for their sake / identifying faces and collecting mass data ...
> Sounds like a blackbox that assumes I want to ski ... because some pics look "like" skiing or something. Or tries to say connect someone with their ex or people who look like their ex ...
Kinda. But collaborative filtering by the textbook definition tries to find the people who have those embedding and then match those nodes with other similar nodes.
So because you are a person who likes skiing, the system tries to find other people's existing preferences that likes skiing and match those to the items to show you.
https://en.wikipedia.org/wiki/Collaborative_filtering
My gallery has nothing but work-related photos... racks and racks of servers as far as the eye can see.
Theres a particular stereotype for nerds with terabytes of hard drives…
And then there are the objectums. No computer is safe from them.