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Just open-sourced Point·E — DALL·E but for 3D point clouds. Extremely fast to generate: https://t.co/fXrdnxi7Qw pic.twitter.com/PUKRZO9fum
— Greg Brockman (@gdb) 20 décembre 2022
Just open-sourced Point·E — DALL·E but for 3D point clouds. Extremely fast to generate: https://
github.com/openai/point-e
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10 yrs ago @karpathy wrote a blog post on the outlook of AI: https://
karpathy.github.io/2012/10/22/sta
te-of-computer-vision/
… in which he describes how difficult it would be for an AI to understand a given photo, concluding "we are very, very far and this depresses me."
Today, our Flamingo steps up to the challenge.
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The "Parents AI test". My dad just sent me a picture and told me: "there's no way AI can guess what this is". Challenge accepted! #Flamingo
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Congratulations to the authors of “Do As I Can, Not As I Say: Grounding Language in Robotic Affordances” (see the blog at https://
goo.gle/3QRJhgl) for winning the Special Innovation Award at @corl_conf
! #CoRL2022
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Really really really hard problem to solve, need a massive database of furniture objects and then recognize them, I have no idea how to that, tried to learn it but it's very difficult
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Maryland U & NYU’s Visual Exploration Reveals What Vision Transformers Learn https://
syncedreview.com/2022/12/18/mar
yland-u-nyus-visual-exploration-reveals-what-vision-transformers-learn/
…
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The weekend is not the same without "Meme-orable" Sunday!
#memes #aimemes #AI #funny #computervision
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2/Human referees have struggled to accurately call violations of the off-side rule — a complex rule involving positions of multiple players and the ball. This is where computer vision steps in.
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False. It's finetuned on spectograms of songs from diff music genres
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Yep, resembles the artifacts I had here. I get a lot of zig zags too.
— Gene Kogan (@genekogan) 16 décembre 2022
I think our working hypothesis was that the output image gradually careens out of the training distribution, so even after noising it, SD doesn't quite know what to do with it.https://t.co/r1YZNzonfU
Yep, resembles the artifacts I had here. I get a lot of zig zags too. I think our working hypothesis was that the output image gradually careens out of the training distribution, so even after noising it, SD doesn't quite know what to do with it.